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1
Ajaz, Taufeeq. "Nonlinear Reaction functions: Evidence from India." Journal of Central Banking Theory and Practice 8, no. 1 (January 1, 2019): 111–32. http://dx.doi.org/10.2478/jcbtp-2019-0006.
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Abstract This paper uses time-series data from India and tests for asymmetries in policy preferences of the Reserve Bank of India (the Central Bank of India, hereafter RBI). The results show evidence in favour of preference asymmetries in monetary policy reaction function in India and hence nonlinearities in the Taylor-rule. Evidence of both recession avoidance preference (RAP) as well as inflation avoidance preference (IAP) is established. And it is found that RAP is dominant over IAP, thus confirming nonlinearities in reaction function which in the present case turns out to be concave in inflation and output gap. Further, the results indicate preference asymmetries in both the objectives. The coefficient weights to positive and negative inflation and output gap differ over long time horizons thus confirming asymmetric policy preferences. Specifically the RBI seems to be more averse to a negative output gap (contraction) as compared to an equal positive gap. In addition, the RBI appears to be more averse to a positive inflation gap as compared to an equal negative gap.
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Khan, Mohammad Faisal, Md Gulzarul Hasan, Abdul Quddoos, Armin Fügenschuh, and Syed Suhaib Hasan. "Goal Programming Models with Linear and Exponential Fuzzy Preference Relations." Symmetry 12, no. 6 (June 3, 2020): 934. http://dx.doi.org/10.3390/sym12060934.
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Goal programming (GP) is a powerful method to solve multi-objective programming problems. In GP the preferential weights are incorporated in different ways into the achievement function. The problem becomes more complicated if the preferences are imprecise in nature, for example ‘Goal A is slightly or moderately or significantly important than Goal B’. Considering such type of problems, this paper proposes standard goal programming models for multi-objective decision-making, where fuzzy linguistic preference relations are incorporated to model the relative importance of the goals. In the existing literature, only methods with linear preference relations are available. As per our knowledge, nonlinearity was not considered previously in preference relations. We formulated fuzzy preference relations as exponential membership functions. The grades or achievement function is described as an exponential membership function and is used for grading levels of preference toward uncertainty. A nonlinear membership function may lead to a better representation of the achievement level than a linear one. Our proposed models can be a useful tool for different types of real life applications, where exponential nonlinearity in goal preferences exists. Finally, a numerical example is presented and analyzed through multiple cases to validate and compare the proposed models. A distance measure function is also developed and used to compare proposed models. It is found that, for the numerical example, models with exponential membership functions perform better than models with linear membership functions. The proposed models will help decision makers analyze and plan real life problems more realistically.
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FEDRIZZI, MARIO, MICHELE FEDRIZZI, and R. A. MARQUES PEREIRA. "CONSENSUS MODELLING IN GROUP DECISION MAKING: DYNAMICAL APPROACH BASED ON FUZZY PREFERENCES." New Mathematics and Natural Computation 03, no. 02 (July 2007): 219–37. http://dx.doi.org/10.1142/s1793005707000744.
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The. notion of consensus plays an important role in group decision making, particularly when the collective preference structure is generated by a dynamical aggregation process of the single individual preference structures. In this dynamical process of aggregation each single decision maker gradually transforms his/her preference structure by combining it, through iterative weighted averaging, with the preference structures of the remaining decision makers. In this way, the collective decision emerges dynamically as a result of the consensual interaction among the various decision makers in the group. From the point of view of applied mathematics, the models of consensual dynamics stand in the context of multi-agent complex systems, with interactive and nonlinear dynamics. The consensual interaction among the various agents (decision makers) acts on their state variables (the preferences) in order to optimize an appropriate measure of consensus, which can be of type 'hard' (unanimous agreement within the group of decision makers) or 'soft' (partial agreement within the group of decision makers). In this paper, we study the modelling of consensus reaching when the individual testimonies are assumed to be expressed as fuzzy preference relations. Here consensus is meant as the degree to which most of the experts agree on the preferences associated to the most relevant alternatives. First of all we derive a degree of dissensus based on linguistic quantifiers and then we introduce a form of network dynamics in which the quantifiers are represented by scaling functions. Finally, assuming that the decision makers can express their preferences in a more flexible way, i.e. by using triangular fuzzy numbers, we describe the iterative process of opinion transformation towards consensus via the gradient dynamics of a cost function expressed as a linear combination of a dissensus cost function and an inertial cost function.
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Wang, Jing, Bing Yan, Guohao Wang, and Liying Yu. "Rating TAs in fuzzy QFD by objective penalty function and fuzzy TOPSIS based on weighted Hamming distance." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 3665–79. http://dx.doi.org/10.3233/jifs-191955.
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Quality function deployment (QFD) is an useful tool to solve Multi-criteria decision making, which can translate customer requirements (CRs) into the technical attributes (TAs) of a product and helps maintain a correct focus on true requirements and minimizes misinterpreting customer needs. In applying quality function deployment, rating technical attributes from input variables is a crucial step in fuzzy environments. In this paper, a new approach is developed, which rates technical attributes by objective penalty function and fuzzy technique for order preference by similarity to an ideal solution (TOPSIS) based on weighted Hamming distance under the case of uncertain preference characteristics of decision makers in fuzzy quality function deployment. A pair of nonlinear programming models with constraints and a relevant pair of nonlinear programming models with unconstraints called objective penalty function models are proposed to gain the fuzzy important numbers of technical attributes. Then, this paper compares the fuzzy numbers by fuzzy technique for order preference by similarity to an ideal solution (TOPSIS) method based on weighted Hamming distance in consideration of the uncertain preference characteristics of decision makers. To end with, the developed method is examined with the numerical examples.
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Aragón, Edilean Kleber da Silva Bejarano, and Marcelo Savino Portugal. "Nonlinearities in Central Bank of Brazil's reaction function: the case of asymmetric preferences." Estudos Econômicos (São Paulo) 40, no. 2 (June 2010): 373–99. http://dx.doi.org/10.1590/s0101-41612010000200005.
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This paper investigates the existence of possible asymmetries in the Central Bank of Brazil's objectives. By assuming that the loss function is asymmetric with regard to positive and negative deviations of the output gap and of the inflation rate from its target, we estimated a nonlinear reaction function which allows identifying and checking the statistical significance of asymmetric parameters in the monetary authority's preferences. For years 2000 to 2007, results indicate that the Central Bank of Brazil showed asymmetric preference over an above-target inflation rate. Given that this behavior may stem from policy decisions in periods of severe crises (e.g., in 2001 and in 2002), we restricted our sample to the 2004-2007 period. We did not find any empirical evidence of any type of asymmetry in the preferences over the stabilization of inflation and of the output gap for this period.
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Pratikto, Fransiscus Rian, Gerardus Daniel Julianto, and Sani Susanto. "Preference-Based Revenue Optimization for App-Based Lifestyle Membership Plans." Jurnal Ilmiah Teknik Industri 20, no. 1 (July 2, 2021): 21–31. http://dx.doi.org/10.23917/jiti.v20i1.13312.
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The demand for a product is rooted in the consumers’ needs and preferences. Therefore, a pricing optimization model will be more valid if the demand function is represented under this basic notion. A preference-based revenue optimization model for an app-based lifestyle membership program is developed and solved in this research. The model considers competitor products and cannibalization effect from products in other fare-class, where both are incorporated using a preference-based demand function. The demand function was derived through a randomized first choice simulation that converts individual utility values into personal choices based on the random parameter logit model. Cannibalizing products are considered as competing products in the simulation scenario. In the pricing optimization, two and three fare classes based on the membership period are considered. The corresponding pricing optimization problem is a mixed-integer nonlinear programming problem with a solution-dependent objective function. Using enumeration, the three-fare-class optimal prices of Rp420,000, Rp300,000, and Rp60,000 for 12-month, 6-month, and 1-month membership, respectively, are better than those of the two-fare-class. Under this policy, the estimated total revenue is Rp30.56 billion, 41.74% greater than that of the current condition.
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CHEN, TING-YU. "NONLINEAR ASSIGNMENT-BASED METHODS FOR INTERVAL-VALUED INTUITIONISTIC FUZZY MULTI-CRITERIA DECISION ANALYSIS WITH INCOMPLETE PREFERENCE INFORMATION." International Journal of Information Technology & Decision Making 11, no. 04 (July 2012): 821–55. http://dx.doi.org/10.1142/s0219622012500228.
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In the context of interval-valued intuitionistic fuzzy sets, this paper develops nonlinear assignment-based methods to manage imprecise and uncertain subjective ratings under incomplete preference structures and thereby determines the optimal ranking order of the alternatives for multiple criteria decision analysis. By comparing each interval-valued intuitionistic fuzzy number's score function, accuracy function, membership uncertainty index, and hesitation uncertainty index, a ranking procedure is employed to identify criterion-wise preference of alternatives. Based on the criterion-wise rankings and a set of known but incomplete information about criterion weights, a nonlinear assignment model is constructed to estimate criterion weights and to order the priority of various alternatives. Considering multiple criteria evaluation problems with preference conflict about criterion importance, an integrated nonlinear programming model is further established with regard to incomplete and inconsistent weight information. These proposed nonlinear assignment-based methods can obtain an aggregate ranking that effectively combines the relative performance of each alternative in each criterion. In addition, this overall ranking most closely agrees with the criterion-wise rankings. Finally, the feasibility of the proposed method is illustrated by a practical example of selecting a suitable bridge construction method.
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Liu, Huazhen, Wei Wang, Yihan Zhang, Renqian Gu, and Yaqi Hao. "Neural Matrix Factorization Recommendation for User Preference Prediction Based on Explicit and Implicit Feedback." Computational Intelligence and Neuroscience 2022 (January 10, 2022): 1–12. http://dx.doi.org/10.1155/2022/9593957.
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Explicit feedback and implicit feedback are two important types of heterogeneous data for constructing a recommendation system. The combination of the two can effectively improve the performance of the recommendation system. However, most of the current deep learning recommendation models fail to fully exploit the complementary advantages of two types of data combined and usually only use binary implicit feedback data. Thus, this paper proposes a neural matrix factorization recommendation algorithm (EINMF) based on explicit-implicit feedback. First, neural network is used to learn nonlinear feature of explicit-implicit feedback of user-item interaction. Second, combined with the traditional matrix factorization, explicit feedback is used to accurately reflect the explicit preference and the potential preferences of users to build a recommendation model; a new loss function is designed based on explicit-implicit feedback to obtain the best parameters through the neural network training to predict the preference of users for items; finally, according to prediction results, personalized recommendation list is pushed to the user. The feasibility, validity, and robustness are fully demonstrated in comparison with multiple baseline models on two real datasets.
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Izawa, Andrew, and Matthias Fripp. "Multi-Objective Control of Air Conditioning Improves Cost, Comfort and System Energy Balance." Energies 11, no. 9 (September 8, 2018): 2373. http://dx.doi.org/10.3390/en11092373.
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A new model predictive control (MPC) algorithm is used to select optimal air conditioning setpoints for a commercial office building, considering variable electricity prices, weather and occupancy. This algorithm, Cost-Comfort Particle Swarm Optimization (CCPSO), is the first to combine a realistic, smooth representation of occupants’ willingness to pay for thermal comfort with a bottom-up, nonlinear model of the building and air conditioning system under control. We find that using a quadratic preference function for temperature can yield solutions that are both more comfortable and lower-cost than previous work that used a “brick wall” preference function with no preference for further cooling within an allowed temperature band and infinite aversion to going outside the allowed band. Using historical pricing data for a summer month in Chicago, CCPSO provided a 1% reduction in costs vs. a similar “brick-wall” MPC approach with the same comfort and 6–11% reduction in costs vs. other control strategies in the literature. CCPSO can also be used to operate the building with much greater comfort and costs or much lower costs and comfort than the “brick-wall” approach, depending on user preferences. CCPSO also reduced peak-hours demand by 3% vs. the “brick-wall” strategy and 4–14% vs. other strategies. At the same time, the CCPSO strategy increased off-peak energy consumption by 15% or more vs. other control methods. This may be valuable for power systems integrating large amounts of renewable power, which can otherwise become uneconomic due to saturation of demand during off-peak hours.
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Wang, Hua Kai. "Research of Resonant Machine Nonlinear Vibrating Characteristics." Applied Mechanics and Materials 494-495 (February 2014): 641–44. http://dx.doi.org/10.4028/www.scientific.net/amm.494-495.641.
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Studying the core component of the resonant machine by building the bench test system, utilizing the nonlinear analysis function of Abaqus/Explicit to solve the problem that the rigid-flexible coupling model can`t take the material nonlinearity into adequate consideration. Simulating the nonlinearity of the vibrating beam by doing time integration over the kinematic equation to obtain an accurate and reliable result. Firstly, simplifying the bench testing system to simulating the low frequent vibration; And then, comparing the acceleration root value between the test data and the simulating result on the characteristically point to check the reliability and accuracy of the simulation; By studying the hammer vibrating amplitude curve and shear stress curve changing along the rubber spring thickness, providing a preference for the optimization.
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Phruksaphanrat, Busaba, and Ario Ohsato. "Effective Linear Calculational Method for Nonlinear Optimization with a Convex Polyhedral Objective Function and Linear Constraints." Journal of Advanced Computational Intelligence and Intelligent Informatics 6, no. 1 (February 20, 2002): 7–18. http://dx.doi.org/10.20965/jaciii.2002.p0007.
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This research proposes an effective linear calculational method based on convex cone concept for solving non-linear optimization problems with a convex polyhedral objective function and linear constraints. One familiar type of convex polyhedral objective functions is a triangular type, which can be normally solved by weighted goal programming (WGP). The necessary preference information of WGP is weights of positive and negative deviational variables. Alternatively, the linear calculational method prefers the other operational way in designing of an objective function by deviational constants, which is practical for the decision maker. For convex polyhedral type objective function problems, conventionally separable convex programming and goal programming (GP) are applied. By separable convex programming, it needs to separate the objective function into line segments before solving the problem, which means increasing of variables and constraints. In case of GP each breakpoint is determined as a goal so the number of constraints and the deviational variables are drastically increased. By the effective linear calculational method proposed in this paper, the problem could be simply formulated to the linear programming problem, which is easy for the decision maker to apply. Moreover this method has lower number of constraints and variables than existing methods so the calculational time can also be reduced.
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Molchanova, E. I., E. N. Korzhova, V. V. Fedorov, and A. D. Portnyagin. "Assessment of the possibility of modeling nonlinear multivariable calibration function using artificial neural networks." Industrial laboratory. Diagnostics of materials 87, no. 10 (October 18, 2021): 12–17. http://dx.doi.org/10.26896/1028-6861-2021-87-10-12-17.
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The use of artificial neural networks (ANNs) is considered justified when studying the problems that do not have a generally accepted solution algorithm. One of such problems in X-ray fluorescence analysis (XRF) is a control of the metal content in atmospheric air and air of the working area. Determination of the calibration characteristics is raveled by the lack of standard samples of the composition of aerosols collected on the filter. To solve this problem, synthetic calibration samples (CS) were manufactured as a thin organic film containing a powder material of the known chemical composition. The weight of the film samples varied within a range of 40 – 155 mg to simulate different aerosol loading of the filters and the content of components in them changed 20 – 200 times which corresponds to the samples of real aerosols. The possibility of modeling a nonlinear calibration multivariable function using artificial neural networks was evaluated in analysis of 38 film calibration samples (from 40 to 100 mg). The structure of the neural network, activation functions, learning algorithms have been investigated. Modeling was performed using an academic version of the BaseGroup Deductor analytical platform. It is shown that implementation of the back propagation of errors leads to much higher values of the error of analysis compared to the error of the regression calibration functions, whereas the Resilient Propagation algorithm provides the smallest values of the error of vanadium determination (Sr) in the calibration samples of aerosols. The range of low content of the elements in the training set is determined with a greater error compared to high content range, and therefore, the sigmoid activation function leads to unsatisfactory accuracy of the analysis results, and preference should be given to hyperbolic tangent (tanh).
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BAKER, CURTIS L. "Linear filtering and nonlinear interactions in direction-selective visual cortex neurons: A noise correlation analysis." Visual Neuroscience 18, no. 3 (May 2001): 465–85. http://dx.doi.org/10.1017/s0952523801183136.
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Spatial and temporal properties related to direction selectivity of both simple and complex type visual cortex neurons were assessed by cross-correlation analysis of their responses to random ternary white noise. This stimulus consisted of multiple randomly placed bars, each colored white, black, or gray with equal probability, which were rerandomized every 5–10 ms. A first-order cross-correlation analysis of a neuron's spike train with the spatiotemporal history of the stimulus provided an estimate of the neuron's linear spatiotemporal filtering properties. A nonlinear correlation analysis measured the amount of interaction for pair-wise combinations of bars as a function of their relative spatial and temporal separations. The spatiotemporal orientation of each of these functions was quantified using a “motion energy index” (MEI), which was compared to the neurons' direction selectivity measured with drifting sinewave gratings. Both first-order and nonlinear correlation plots usually showed s–t orientation whose sign was consistent with the neuron's direction preference; however, in many cases the MEI for first-order analysis was weak compared to that seen in the nonlinear interactions. The structures of the nonlinear interaction functions were also compared with predictions from a conventional model of direction selectivity based on a simple spatiotemporally oriented linear filter, followed by an intensive nonlinearity (“LN model”). These comparisons showed that some neurons' data agreed reasonably well with such a model, while others agreed poorly or not at all. Simulations of an alternative model which combines signals from idealized lagged and nonlagged front-end linear filters produce noise correlation results more like those seen in the neurophysiological data.
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Chadee, Aaron Anil, Xsitaaz T. Chadee, Clyde Chadee, and Festus Otuloge. "Violations at the Reference Point of Discontinuity: Limitations of Prospect Theory and an Alternative Model of Risk Choices." Emerging Science Journal 6, no. 1 (February 1, 2022): 37–52. http://dx.doi.org/10.28991/esj-2022-06-01-03.
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The tilted S-shaped utility function proposed in Prospect Theory (PT) relied fundamentally on the geometrical notion that there is a discontinuity between gains and losses, and that individual preferences change relative to a reference point. This results in PT having three distinct parameters; concavity, convexity and the reference point represented as a disjoint between the concavity and convexity sections of the curve. The objective of this paper is to examine the geometrical violations of PT at the zero point of reference. This qualitative study adopted a theoretical review of PT and Markowitz’s triply inflected value function concept to unravel methodological assumptions which were not fully addressed by either PT or cumulative PT. Our findings suggest a need to account for continuity and to resolve this violation of PT at the reference point. In so doing, an alternative preference transition theory, was proposed as a solution that includes a phase change space to cojoin these three separate parameters into one continuous nonlinear model. This novel conceptual model adds new knowledge of risk and uncertainty in decision making. Through a better understanding of an individual’s reference point in decision making behaviour, we add to contemporary debate by complementing empirical studies and harmonizing research in this field. Doi: 10.28991/ESJ-2022-06-01-03 Full Text: PDF
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Bastin, Fabian, Cinzia Cirillo, and Stephane Hess. "Evaluation of Optimization Methods for Estimating Mixed Logit Models." Transportation Research Record: Journal of the Transportation Research Board 1921, no. 1 (January 2005): 35–43. http://dx.doi.org/10.1177/0361198105192100105.
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The performances of different simulation-based estimation techniques for mixed logit modeling are evaluated. A quasi–Monte Carlo method (modified Latin hypercube sampling) is compared with a Monte Carlo algorithm with dynamic accuracy. The classic Broyden–Fletcher–Goldfarb–Shanno (BFGS) optimization algorithm line-search approach and trust region methods, which have proved to be extremely powerful in nonlinear programming, are also compared. Numerical tests are performed on two real data sets: stated preference data for parking type collected in the United Kingdom, and revealed preference data for mode choice collected as part of a German travel diary survey. Several criteria are used to evaluate the approximation quality of the log likelihood function and the accuracy of the results and the associated estimation runtime. Results suggest that the trust region approach outperforms the BFGS approach and that Monte Carlo methods remain competitive with quasi–Monte Carlo methods in high-dimensional problems, especially when an adaptive optimization algorithm is used.
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Martins, André C. R. "Network generation and evolution based on spatial and opinion dynamics components." International Journal of Modern Physics C 30, no. 09 (September 2019): 1950077. http://dx.doi.org/10.1142/s0129183119500773.
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In this paper, a model for a spatial network evolution based on a Metropolis simulation using an energy function that depends both on the distance between the nodes and the stated preferences is studied. The agents influence their network neighbors using the CODA model, where each agent has a preference between two options based on its probabilistic assessment of which option is the best one. The algorithm generates realistic networks for opinion problems as well as temporal dynamics for those networks. The transition of a random state to an ordered situation, as temperature decreases, leads to different outcomes depending on the relative strength of the spatial and opinion components of the energy.
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He, Qizhi, Pingfan Xia, Bo Li, and Jia-Bao Liu. "Evaluating Investors’ Recognition Abilities for Risk and Profit in Online Loan Markets Using Nonlinear Models and Financial Big Data." Journal of Function Spaces 2021 (September 30, 2021): 1–15. http://dx.doi.org/10.1155/2021/5178970.
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Financial big data are obtained by web crawler, and investors’ recognition abilities for risk and profit in online loan markets are researched using heteroskedastic Probit models. The conclusions are obtained as follows: First, the preference for the item is reflected directly in the time and indirectly in the number of participants for being full, and the larger the preference, the shorter the time and the fewer the participants. Second, investors can discriminate the default risk not reflected by the interest rate, and the bigger the default risk, the longer the time and the more participants being full. Third, investors can discriminate the pure return rate deducted from the maturity term and credit risk, and the higher the return, the shorter the time and the fewer the participants being full. Fourth, default risks are reflected well by online loan platform interest rates, and inventors do not choose the item blindly according to the interest rate but consider comprehensively the profit and the risk. In the future, interest rate liberalization should be deepened, the choosing function of interest rates should be played better, and the information disclosure, investor education, and investor effective usage of other information should be strengthened.
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ABO-SINNA, MAHMOUD A., and AZZA H. AMER. "TOPSIS Approach for Solving Bi-Level Non-Linear Fractional MODM Problems." JOURNAL OF ADVANCES IN MATHEMATICS 13, no. 4 (February 9, 2018): 7353–70. http://dx.doi.org/10.24297/jam.v13i4.6243.
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TOPSIS (technique for order preference similarity to ideal solution) is considered one of the known classical multiple criteria decision making (MCDM) methods to solve bi-level non-linear fractional multi-objective decision making (BL-NFMODM) problems, and in which the objective function at each level is considered nonlinear and maximization type fractional functions. The proposed approach presents the basic terminology of TOPSIS approach and the construction of membership function for the upper level decision variable vectors, the membership functions of the distance functions from the positive ideal solution (PIS) and of the distance functions from the negative ideal solution (NIS). Thereafter a fuzzy goal programming model is adopted to obtain compromise optimal solution of BL-NFMODM problems. The proposed approach avoids the decision deadlock situations in decision making process and possibility of rejecting the solution again and again by lower level decision makers. The presented TOPSIS technique for BL-NFMODM problems is a new fuzzy extension form of TOPSIS approach suggested by Baky and Abo-Sinna (2013) (Applied Mathematical Modelling, 37, 1004-1015, 2013) which dealt with bi -level multi-objective decision making (BL-MODM) problems. Also, an algorithm is presented of the new fuzzy TOPSIS approach for solving BL-NFMODM problems. Finally, an illustrative numerical example is given to demonstrate the approach.
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Wang, C., B. Yang, and H. Q. Wang. "Multi-Objective Master Production Schedule for Balanced Production of Manufacturers." International Journal of Simulation Modelling 19, no. 4 (December 5, 2020): 678–88. http://dx.doi.org/10.2507/ijsimm19-4-co17.
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Focusing on the balanced use of production capacity in the formulation of master production schedule (MPS), this paper sets up a single-product, multi-stage, multi-objective MPS model based on balanced production. Whereas the model aims to achieve multiple objectives through nonlinear integer programming, a genetic algorithm based on automatic transformation (AT-GA) was designed to solve the model. Specifically, the chromosomes were encoded as integers to satisfy the model constraints; the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was adopted to handle the four nonlinear objectives of the model, thereby obtaining the fitness function; the fuzzy logic control (FLC) was introduced to automatically adjust the crossover and mutation parameters, and balance the global and local search abilities of the GA, enhancing the computing power of the algorithm. The experimental results show that the AT-GA can effectively solve the multi-objective MPS optimization problem under balanced production.
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Huo, Jiuyuan, and Liqun Liu. "Evaluation Method of Multiobjective Functions’ Combination and Its Application in Hydrological Model Evaluation." Computational Intelligence and Neuroscience 2020 (March 10, 2020): 1–23. http://dx.doi.org/10.1155/2020/8594727.
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Parameter optimization of a hydrological model is intrinsically a high dimensional, nonlinear, multivariable, combinatorial optimization problem which involves a set of different objectives. Currently, the assessment of optimization results for the hydrological model is usually made through calculations and comparisons of objective function values of simulated and observed variables. Thus, the proper selection of objective functions’ combination for model parameter optimization has an important impact on the hydrological forecasting. There exist various objective functions, and how to analyze and evaluate the objective function combinations for selecting the optimal parameters has not been studied in depth. Therefore, to select the proper objective function combination which can balance the trade-off among various design objectives and achieve the overall best benefit, a simple and convenient framework for the comparison of the influence of different objective function combinations on the optimization results is urgently needed. In this paper, various objective functions related to parameters optimization of hydrological models were collected from the literature and constructed to nine combinations. Then, a selection and evaluation framework of objective functions is proposed for hydrological model parameter optimization, in which a multiobjective artificial bee colony algorithm named RMOABC is employed to optimize the hydrological model and obtain the Pareto optimal solutions. The parameter optimization problem of the Xinanjiang hydrological model was taken as the application case for long-term runoff prediction in the Heihe River basin. Finally, the technique for order preference by similarity to ideal solution (TOPSIS) based on the entropy theory is adapted to sort the Pareto optimal solutions to compare these combinations of objective functions and obtain the comprehensive optimal objective functions’ combination. The experiments results demonstrate that the combination 2 of objective functions can provide more comprehensive and reliable dominant options (i.e., parameter sets) for practical hydrological forecasting in the study area. The entropy-based method has been proved that it is effective to analyze and evaluate the performance of different combinations of objective functions and can provide more comprehensive and impersonal decision support for hydrological forecasting.
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Ngoc Thach, Nguyen. "Macroeconomic Growth in Vietnam Transitioned to Market: An Unrestricted VES Framework." Economies 8, no. 3 (July 16, 2020): 58. http://dx.doi.org/10.3390/economies8030058.
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The Vietnamese economy has increased at high speed over the transformation decades; however, most recent studies on the economic growth of this country used the Cobb-Douglas or CES (Constant Elasticity of Substitution) production functions, which are unable to explore the relationship between the elasticity of capital-labour substitution and development process, and hence, are not relevant to accessing a dynamic economic system. For that reason, this study is conducted to specify an unrestricted VES (Variable Elasticity of Substitution) production function in a one-sector growth model of Vietnam, highlighted by two characteristics: successful transition from plan to market and rapid progress. The VES is given preference over the CES and the Cobb-Douglas having the elasticity of substitution between capital and labour varying with economic development. By employing a Bayesian nonlinear regression through MCMC methods, the study reported the following findings: (1) the above-unity variable elasticity of capital-labour substitution in an aggregate unrestricted VES function specified for Vietnam shows that the model generates the possibility of endogenous economic growth; (2) the capital share tends to increase, while the labour share faces a downward trend along with the development of Vietnam; (3) the VES is empirically proven through a Bayes factor test to be superior to the CES and Cobb-Douglas for analysis of the growth process of Vietnam, an emerging transition economy.
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Zhang, Min, Xiaojuan Wang, Lei Jin, and Mei Song. "Cascading failure of interdependent networks with dependence groups obeying different distributions." International Journal of Modern Physics C 31, no. 08 (July 4, 2020): 2050107. http://dx.doi.org/10.1142/s0129183120501077.
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Recent work on the cascading failure of networks with dependence groups assumes that the number of nodes in each dependence group is equal. In this paper, we construct a model on interdependent networks with dependence groups against cascading failure. The size of dependence group is supposed to obey the Poisson Distribution and the Truncated Normal Distribution, respectively. By applying the tools of mean-field approximation and the generating function techniques, the cascading model is theoretically analyzed and the theoretical solutions are nearly consistent with the simulation values. Besides, we define three kinds of coupling preferences based on node degree, i.e. assortative coupling, disassortative coupling and random coupling. The connection between layers is no longer one-to-one correspondence of nodes, but fully connection of some groups. In addition, some factors affecting the network robustness are discussed and extensive simulations are realized on two-layer BA networks. The simulation results show that the coupling preference has influence on the network robustness and the network with dependence groups obeying the Truncated Normal Distribution performs better than the Poisson Distribution.
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Zentani, Abdulhakim Mehemed, Nadiatulhuda Zulkifli, and Arnidza Ramli. "A Weight Influence of Logarithmic and Exponential Functions on the Selection of Wireless Networks using Multi-Criteria Decision-Making Methods." Science Proceedings Series 3, no. 1 (June 7, 2021): 31–36. http://dx.doi.org/10.31580/sps.v3i1.1931.
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This research aims to study the influence of logarithmic and exponential functions on the multi-criteria decision-making algorithm that changes linear method to nonlinear method. It is carried out to better understand the multi-criteria decision-making, namely the technique for preference by similarity to ideal solution (TOPSIS) algorithm whereby in which these functions may influence the criteria weights during the selection of the best network. The investigation is applied under different network types to evaluate the most optimum network that leads to better throughput, low latency, minimum BER, and low price per MB. The algorithms are assessed in MATLAB simulation environments. The study also considered the adoption of the Wi-Fi networks standard which is factors such as bandwidth, signal to noise ratio and the channel modulation technique were determined during the decision-making process. The simulation results show that the exponential function had produced approximately similar results to that of linear TOPSIS algorithm because both methods keep the weights to demonstrate positive values. However, logarithmic TOPSIS produced different results as the weights have negative values which lead to a phase shift of 180⁰ during the decision process. Thus, linear TOPSIS was found to have the best results while logarithmic TOPSIS had the worst outcome.
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Eschenauer, H. A., and W. Fuchs. "Fiber-Reinforced Sandwich Plates Under Static Loads—Proposals for Their Optimization." Journal of Mechanisms, Transmissions, and Automation in Design 108, no. 2 (June 1, 1986): 152–58. http://dx.doi.org/10.1115/1.3260796.
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In order to satisfy the permanently increasing specification demands on machines and plants, the structural members and units of the constructions have to be designed in an “optimal” way. Nowadays more and more composite structures are attaining such new fields of application where one-component materials are not able to fulfill the higher demands. Typical examples are light constructions with optimal stiffness at various loadings. In this paper, a detailed investigation of the deformation behavior of a simply supported sandwich plate under static loads is carried out. Then, an optimal design method is formulated as a nonlinear multiobjective optimization problem by adopting the two conflicting objectives “minimal deformation” at “minimal weight,” including a set of constraints. The application of an optimization strategy is shown by means of a special preference function and sequential linearization as optimizer. Finally, some results of this procedure are discussed concerning the optimal design of sandwich plates.
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Park, Dongjoo, Laurence R. Rilett, and Changho Choi. "A class of multicriteria shortest path problems for real-time in-vehicle routing." Canadian Journal of Civil Engineering 34, no. 9 (September 1, 2007): 1096–109. http://dx.doi.org/10.1139/l07-013.
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In-route guidance systems fastest path routing has typically been adopted because of its simplicity. However, empirical studies on route choice behavior have shown that drivers use numerous criteria in choosing a route. The objective of this paper is to develop computationally efficient algorithms for identifying a manageable subset of the nondominated (i.e., Pareto optimal) paths for real-time in-vehicle routing. The basic notion of the proposed approach is that (i) enumerating all nondominated paths is computationally too expensive, (ii) obtaining a stable mathematical representation of the driver's utility function is theoretically difficult and impractical, and (iii) identifying the optimal path given a nonlinear utility function is a nondeterministic polynomial time (NP)-hard problem. Consequently, a heuristic two-stage strategy that identifies multiple routes and then selects the near-optimal path may be effective and practical. As the first stage, we relax the uniqueness of the utility function by measuring the context-dependent preference using an entropy model and propose a branch-and-bound technique that discards most of the nondominated paths. To make sure that the paths identified are dissimilar in terms of links used, the portion of shared links between routes is limited. The test of the algorithm in a large real-life traffic network shows that the algorithm can significantly reduce computational complexity while identifying reasonable alternative paths. Key words: real-time vehicle routing, multiple routes, utility function, optimal path.
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Wang, Ning, Mohammed Abouheaf, Wail Gueaieb, and Nabil Nahas. "Model-Free Optimized Tracking Control Heuristic." Robotics 9, no. 3 (June 29, 2020): 49. http://dx.doi.org/10.3390/robotics9030049.
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Many tracking control solutions proposed in the literature rely on various forms of tracking error signals at the expense of possibly overlooking other dynamic criteria, such as optimizing the control effort, overshoot, and settling time, for example. In this article, a model-free control architectural framework is presented to track reference signals while optimizing other criteria as per the designer’s preference. The control architecture is model-free in the sense that the plant’s dynamics do not have to be known in advance. To this end, we propose and compare four tracking control algorithms which synergistically integrate a few machine learning tools to compromise between tracking a reference signal and optimizing a user-defined dynamic cost function. This is accomplished via two orchestrated control loops, one for tracking and one for optimization. Two control algorithms are designed and compared for the tracking loop. The first is based on reinforcement learning while the second is based on nonlinear threshold accepting technique. The optimization control loop is implemented using an artificial neural network. Each controller is trained offline before being integrated in the aggregate control system. Simulation results of three scenarios with various complexities demonstrated the effectiveness of the proposed control schemes in forcing the tracking error to converge while minimizing a pre-defined system-wide objective function.
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Jiang, Xueping, Jen-Mei Chang, and Hui Sun. "Inframarginal Model Analysis of the Evolution of Agricultural Division of Labor." Mathematics 7, no. 12 (December 1, 2019): 1152. http://dx.doi.org/10.3390/math7121152.
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Division of labor plays a critical role in many parts of agriculture. For example, a specialized division of labor can lead to the improvement of labor productivity, the reduction of production costs, and the innovation of production technology and organization. At the heart of agricultural management is how the comparative advantages of farmers impact their production decision-making behavior, and, consequently, influence the division of labor structure. In this paper, we apply an infra-marginal model to interpret the selection logic of heterogeneous farmers’ specialized production with exogenous comparative technical advantages and transaction costs. Solving the nonlinear programming problem of the utility function within each respective labor structure leads to a corner equilibrium. Under reasonable assumptions of the model, we reduced the number of possible production–consumption decision modes from the maximum of 64 to an optimal of 3. Through this analysis, we discovered the ranges for transaction efficiency coefficients and preference parameter under which each structure can achieve general equilibrium. Our theoretical model thereby explains the structural evolution of agricultural division of labor.
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Zhu, Hanbo, and Changqing Miao. "Seismic Fragility Analysis of the Reinforced Concrete Continuous Bridge Piers Based on Machine Learning and Symbolic Regression Fusion Algorithms." Shock and Vibration 2021 (December 6, 2021): 1–13. http://dx.doi.org/10.1155/2021/8969389.
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In the fragility analysis, researchers mostly chose and constructed seismic intensity measures (IMs) according to past experience and personal preference, resulting in large dispersion between the sample of engineering demand parameter (EDP) and the regression function with IM as the independent variable. This problem needs to be solved urgently. Firstly, the existing 46 types of ground motion intensity measures were taken as a candidate set, and the composite intensity measures (IMs) based on machine learning methods were selected and constructed. Secondly, the modified Park–Ang damage index was taken as EDP, and the symbolic regression method was used to fit the functional relationship between the composite intensity measures (CIMs) and EDP. Finally, the probabilistic seismic demand analysis (PSDA) and seismic fragility analysis were performed by the cloud-stripe method. Taking the pier of a three-span continuous reinforced concrete hollow slab bridge as an example, a nonlinear finite element model was established for vulnerability analysis. And the composite IM was compared with the linear composite IM constructed by Kiani, Lu Dagang, and Liu Tingting. The functions of them were compared. The analysis results indicated that the standard deviation of the composite IM fragility curve proposed in this paper is 60% to 70% smaller than the other composite indicators which verified the efficiency, practicality, proficiency, and sufficiency of the proposed machine learning and symbolic regression fusion algorithms in constructing composite IMs.
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Hutcheon, B., R. M. Miura, Y. Yarom, and E. Puil. "Low-threshold calcium current and resonance in thalamic neurons: a model of frequency preference." Journal of Neurophysiology 71, no. 2 (February 1, 1994): 583–94. http://dx.doi.org/10.1152/jn.1994.71.2.583.
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1. We constructed a mathematical model of the subthreshold electrical behavior of neurons in the nucleus mediodorsalis thalami (MDT) to elucidate the basis of a Ni(2+)-sensitive low-frequency (2-4 Hz) resonance found previously in these neurons. 2. A model that included the low- and high-threshold Ca2+ currents (IT and IL), a Ca(2+)-activated K+ current (IC), a rapidly inactivating K+ current (IA), a voltage-dependent K+ current which we call IKx, and a voltage-independent leak current (Il), successfully simulated the low-threshold spike observed in MDT neurons. This model (the MDT model) and a minimal version of the model containing only IT and I1 (the minimal MDT model) were used in the analysis. 3. An impedance function was derived for a linearized version of the MDT model. This showed that the model predicts a low-frequency (2-4 Hz) resonance in the voltage response to "small" oscillatory current inputs (producing voltage changes of < 10 mV) when the membrane potential is between -60 and -85 mV. 4. Further examination of the impedances for the MDT and minimal MDT models shows that IT underlies the frequency- and voltage-dependent resonance. The slow inactivation of IT results in an attenuation of voltage responses to low frequencies, resulting in a band-pass behavior. The fast activation of IT amplifies the resonance and modulates the peak frequency but does not, in itself, cause resonance. 5. When voltage responses are small (< 10 mV), the strength and voltage-dependence of resonance of the minimal MDT model are determined by the steady-state window conductance, gw, due to IT. This steady-state conductance arises where the steady-state activation, m(infinity2)(V), and inactivation, h(infinity) (V), curves overlap. Parallel shifts in the inactivation curve can eliminate or enhance resonance with little effect on the IT-dependent low-threshold spike evoked after hyperpolarizing current pulses. When the peak magnitude of gw was large, the minimal MDT model showed spontaneous oscillations at 3 Hz with amplitudes > 30 mV. 6. Large oscillatory current inputs evoked significantly nonlinear voltage responses in the minimal MDT model, but the 2- to 4-Hz frequency selectivity (predicted from the linearized impedance) remained. 7. We conclude that the properties of the low-threshold Ca2+ current, IT, are sufficient to explain the Ni(2+)-sensitive 2- to 4-Hz resonance seen in MDT neurons.(ABSTRACT TRUNCATED AT 400 WORDS)
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Ghamami, Mehrnaz, and MohammadHossein (Sam) Shojaei. "Introducing a Design Framework for a Multi-Modal Public Transportation System, Focusing on Mixed-Fleet Bike-Sharing Systems." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 36 (October 8, 2018): 103–15. http://dx.doi.org/10.1177/0361198118799170.
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Bike-sharing is increasingly becoming more popular. Electric bikes as an emerging transportation technology have extended range and are less physically demanding, compared with regular bicycles, thus they can be incorporated into regular bike-sharing systems to attract more users. This study aims at capturing the users’ preference, while considering investors’ limitations and societal cost and benefits of each mode. The problem is defined as a mixed-integer non-liner problem, with nonlinear objective function and constraints. Because of the computationally challenging nature of the problem, a metaheuristic algorithm based on simulated annealing algorithm is proposed for its solution. The performance of the algorithm is tested in this study and convergence patterns are observed. The main findings of this study which are derived from the hypothetical numerical example, include but are not limited to: (1) the most popular public modes are bus and pedelec, because these two modes (bus and pedelec) are less expensive and have the ability to traverse longer distances in comparison to similar modes (i.e., e-scooter/car and bike), and (2) for small communities with short travel distances (feasible within the ranges of active modes), users would not choose fuel-consuming modes, and thus their choice is insensitive to fuel cost.
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Gooler, D. M., and A. S. Feng. "Temporal coding in the frog auditory midbrain: the influence of duration and rise-fall time on the processing of complex amplitude-modulated stimuli." Journal of Neurophysiology 67, no. 1 (January 1, 1992): 1–22. http://dx.doi.org/10.1152/jn.1992.67.1.1.
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1. Single-unit recordings were made in the auditory midbrain, the torus semicircularis (TS) of the northern leopard frog, to independently characterize the processing of different temporal attributes (signal duration, rise-fall time, and rate of amplitude modulation) of natural sounds and to investigate how these temporal variables interact to produce the observed responses to complex amplitude-modulated (AM) signals. Response functions, on the basis of mean spike count, were derived and categorized to describe the unit's temporal response characteristics to each of the variables. 2. To characterize the duration response functions, tone bursts of different durations (stimuli repeated at a constant repetition rate) at the unit's characteristic frequency (CF) and 10 dB above minimum threshold at CF (MT) were presented monaurally to the contralateral ear. The duration response function of a TS neuron was often related to the temporal discharge characteristics of the neuron. Increases in stimulus duration elicited an increase in spike counts (therefore, long-pass response function) from most neurons (74%) in the TS; 91% of these neurons showed tonic discharge patterns. Phasic-burst (PB) cells that were rapidly adapting showed long-pass duration response functions that were highly nonlinear, having peaks and notches embedded within the functions. On the other hand, one-third of phasic neurons tended to be insensitive to stimulus duration, giving similar spike counts in response to stimuli of greatly different durations (i.e., all pass). In the TS, some neurons (9%) only responded to a limited range of durations (i.e., band-duration pass), and still others showed a preference for shorter durations (9%; i.e., short pass); these were exhibited primarily by phasic and PB neurons. 3. To characterize the rise-fall time response functions, tone bursts having different rise-fall times were presented. The rise-fall time response functions of TS neurons had two distinct characteristics. The majority of tonic cells (91%), as well as some PB (38%) and phasic (29%) neurons, gave essentially invariant spike counts for all stimulus rise-fall times (i.e., all pass; 73% of neurons). Despite the relatively stable spike counts of neurons showing all-pass functions, the peristimulus time histograms (PSTHs) deriving from responses to slower rise-fall time stimuli exhibited a longer and somewhat more variable onset latency. About one-fourth (27%) of TS neurons, mostly phasic and PB neurons, showed higher spike counts for signals with rapid rise-fall times.(ABSTRACT TRUNCATED AT 400 WORDS)
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Gryzin, S. V. "The research of the influence of digital exchange in the stabilization system of the unmanned aerial vehicle on the dynamic characteristics of the steering actuator." Civil Aviation High Technologies 22, no. 5 (October 28, 2019): 54–66. http://dx.doi.org/10.26467/2079-0619-2019-22-5-54-66.
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When designing a stabilization system for highly maneuverable unmanned aerial vehicles (UAVs), one of the relevant tasks is to impose requirements on the dynamic characteristics and control methods of the steering actuators, which will ensure the required stability margins of the stabilization system as part of the UAV control system. Currently, there is an increasing preference for microcontroller method of electric actuator control and digital exchange between the control system and the steering actuators. One of the reasons for the reduction of stability margins of the stabilization system is the delay introduced by the digital exchange between the elements of the stabilization system. In the process of solving the problem of transition to digital exchange between the elements of the stabilization system, a research was conducted of the influence of amplitude and phase distortions arising in the path "data transmission interface steering actuator" on the dynamic characteristics of the steering actuator. As an actuator of the stabilization system, the real electric drive used on highly maneuverable UAVs is considered. For this drive, extremely stringent requirements for bandwidth and phase delays are introduced, which complicates the problem of ensuring the stability of the stabilization system, taking into account the delays in the digital exchange. As a result of the research, a frequency model has been proposed that allows to estimate the minimum possible exchange rate in the path "data interface steering actuator", taking into account ensuring the required dynamic characteristics of the actuator. In the proposed model, the data transfer interface is represented as a zero-order hold, the transfer function of which is replaced by Pade approximations of the second order. In the course of the research, a comparison was made of the results obtained on the proposed model with the results of experiments on a real electric actuator and its complete nonlinear time model. The main advantage of the proposed frequency model is the simplicity of obtaining the transfer function of the path "data interface steering actuator". This allows at the initial stage of the research to quickly and accurately determine the minimum possible rate of exchange, which will ensure the fulfillment of the requirements imposed on the drive dynamics.
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Hoderlein, Stefan, and Arthur Lewbel. "REGRESSOR DIMENSION REDUCTION WITH ECONOMIC CONSTRAINTS: THE EXAMPLE OF DEMAND SYSTEMS WITH MANY GOODS." Econometric Theory 28, no. 5 (August 16, 2012): 1087–120. http://dx.doi.org/10.1017/s0266466612000412.
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Microeconomic theory often yields models with multiple nonlinear equations, nonseparable unobservables, nonlinear cross equation restrictions, and many potentially multicolinear covariates. We show how statistical dimension reduction techniques can be applied in models with these features. In particular, we consider estimation of derivatives of average structural functions in large consumer demand systems, which depend nonlinearly on the prices of many goods. Utility maximization imposes nonlinear cross equation constraints including Slutsky symmetry, and preference heterogeneity yields demand functions that are nonseparable in unobservables. The standard method of achieving dimension reduction in demand systems is to impose strong, empirically questionable economic restrictions such as separability. In contrast, the validity of statistical methods of dimension-reduction such as principal components has not hitherto been studied in contexts like these. We derive the restrictions implied by utility maximization on dimension-reduced demand systems and characterize the implications for identification and estimation of structural marginal effects. We illustrate the results by reporting estimates of the effects of gasoline prices on the demands for many goods, without imposing any economic separability assumptions.
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Malakooti, Behnam. "Double Helix Value Functions, Ordinal/Cardinal Approach, Additive Utility Functions, Multiple Criteria, Decision Paradigm, Process, and Types (Z Theory I)." International Journal of Information Technology & Decision Making 14, no. 06 (November 2015): 1353–400. http://dx.doi.org/10.1142/s0219622014500412.
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Z Utility Theory refers to a class of nonlinear utility functions for solving Risk and Multiple Criteria Decision-Making problems. Z utility functions are hybrids of additive and nonadditive (nonlinear) functions. This paper addresses the concepts and assessment methods for the additive part of Z-utility functions for multiple criteria problems that satisfy the efficiency (nondominancy) principle. We provide a decision paradigm and guidelines on how to approach, formulate, and solve decision-making problems. We, also, overview the modeling of decision process based on four types of decision-making styles. For multi-criteria problems, a new definition of convex efficiency is introduced. Also polyhedral efficiency is developed for presenting multi-criteria efficiency (nondominancy) graphically. New double helix quasi-linear value functions for multi-criteria are developed. Two types of double helix value functions for solving bi-criteria (Advantages versus Disadvantages) and also risk problems are introduced: Food–Fun curves for expected values and Fight-Flight curves for expected risk values. Ordinal/Cardinal Approach (OCA) for assessment of additive utility functions is developed. Simple consistency tests to determine whether the assessed utility function satisfies ordinal and/or cardinal properties are provided. We show that OCA can also be used to solve outranking problems. We provide a critique of Analytic Hierarchy Process (AHP) for assessing additive value functions and show that the developed Ordinal/Cardinal Approach overcomes the shortcomings of AHP. We also develop a unified/integrated approach for simultaneous assessment of nonlinear value and additive (multi-criteria) utility functions. These results in an additive utility function that can be concave, convex, or hybrid concave/convex based on the nonlinear value function. Finally, we show an interactive paired comparisons approach for solving nonadditive and nonlinear utility functions for bi-criteria decision-making problems. Several illustrative examples are provided. The paper provides reliable and robust approaches for modeling the utility preferences of heterogeneous economic agents in macro and micro-economics.
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Nyumuah, Felix S. "Testing for Asymmetric Central Bank Preferences." International Journal of Economics and Finance 10, no. 4 (March 3, 2018): 25. http://dx.doi.org/10.5539/ijef.v10n4p25.
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The linear specification of the ideal monetary policy reaction function has been questioned in recent times by researchers. They have suggested a nonlinear framework where central banks exhibit asymmetric behaviours. Despite the important policy implications of having asymmetric central bank preferences, studies have been on single-country basis focusing almost entirely on advanced economies. The aim of this study is to check the existence of asymmetric preferences on the part of central banks in the context of a panel of countries and not just a single a country. The study derives and estimates a nonlinear flexible optimal monetary policy rule, which permits zone-like as well as asymmetric behaviours using panel data from a range of countries both developed and less developed. Although the findings indicate the presence of asymmetric preferences on the output gap across less developed countries, generally, the evidence is in favour of a linear policy reaction function and symmetric central bank preferences. These findings mean that monetary policy is characterised by a linear policy rule and symmetric central bank preferences. The results also indicate that interest rate ‘smoothing’ reaction by monetary authorities is more pronounced in less developed countries than in developed ones.
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Linster, Christiane, Qiang Nai, and Matthew Ennis. "Nonlinear effects of noradrenergic modulation of olfactory bulb function in adult rodents." Journal of Neurophysiology 105, no. 4 (April 2011): 1432–43. http://dx.doi.org/10.1152/jn.00960.2010.
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The mammalian main olfactory bulb receives a significant noradrenergic input from the locus coeruleus. Norepinephrine (NE) is involved in acquisition of conditioned odor preferences in neonatal animals, in some species-specific odor-dependent behaviors, and in adult odor perception. We provide a detailed review of the functional role of NE in adult rodent main olfactory bulb function. We include cellular, synaptic, network, and behavioral data and use computational simulations to tie these different types of data together.
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STIPANOVIĆ, DUŠAN M., CHRISTOPHER VALICKA, and ALI E. ABBAS. "CONTROL STRATEGIES FOR PLAYERS IN PURSUIT-EVASION GAMES BASED ON THEIR PREFERENCES." International Game Theory Review 16, no. 02 (April 3, 2014): 1440008. http://dx.doi.org/10.1142/s0219198914400088.
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In this paper, a design of strategies for players in pursuit-evasion games, is provided. The design incorporates players' preferences captured in their goal functions which are constructed using particular functional forms that include multiattribute copulas. The approach provides closed-form strategies for the players governed by nonlinear models affine in control. A number of sufficient conditions based on differential inequalities for either evasion or capture is formulated. Finally, to demonstrate effectiveness of the proposed design, some illustrative simulations with players modeled as nonlinear and nonholonomic unicycles, are provided.
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Legut, Jerzy. "Optimal Fair Division for Measures with Piecewise Linear Density Functions." International Game Theory Review 19, no. 02 (May 2, 2017): 1750009. http://dx.doi.org/10.1142/s0219198917500098.
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A nonlinear programming method is used for finding an optimal fair division of the unit interval [Formula: see text] among [Formula: see text] players. Preferences of players are described by nonatomic probability measures [Formula: see text] with piecewise linear (PWL) density functions. The presented algorithm can be applied for obtaining “almost” optimal fair divisions for measures with arbitrary density functions approximable by PWL functions. The number of cuts needed for obtaining such divisions is given.
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Li, Wu, Guanqi Guo, and Xiaoqiang Zhou. "Generalizing and Integrating TOPSIS and Cook-Seiford Method for Multicriteria Group Decision-Making with Both Cardinal and Ordinal Data." Mathematical Problems in Engineering 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/154848.
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The TOPSIS and Cook-Seiford social choice function are generalized and integrated for multicriteria group decision-making (MCGDM) with both cardinal evaluations and ordinal preferences of the alternatives. Unlike traditional TOPSIS, at first, the group’s positive ideal solution and negative ideal solution under cardinal and ordinal preferences are defined, respectively. Thus the group rankings of the alternatives with respect to each criterion are derived from the individual preferences by the modified group TOPSIS considering the weights of decision makers under each criterion. Then the weighted distance function representing the total inconsistency between the comprehensive rankings of all alternatives and the ones under all criteria is presented after the criteria weights are taken into account. Form the perspective of minimizing the criteria-weighted distance of the rankings, a nonlinear integer programming is developed and transformed into an assignment problem to obtain the final rankings of all alternatives. An illustrative case is presented and some comparisons on the results show that the developed approach is practical and effective. This study extends TOPSIS to group decision-making with ordinal preferences and generalizes Cook-Seiford social choice function to multicriteria decision-making considering the criteria weights and can be a novel benchmark for MCGDM with both cardinal and ordinal data.
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Гайкович, А. И. "Ship optimization using the preferences of the designer." MORSKIE INTELLEKTUAL`NYE TEHNOLOGII), no. 1(51) (March 5, 2021): 68–74. http://dx.doi.org/10.37220/mit.2021.51.1.027.
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В статье рассматривается подход к решению задачи оптимизации характеристик корабля на ранних стадиях проектирования. Задача оптимизационного проектирования формулируется как многокритериальная. Дается краткий обзор подходов к решению задач математического программирования такого класса. Рассматривается такой метод многокритериальной оптимизации, широко используемый в экономических задачах, как оптимизация по Парето. Для решения задач, связанных с созданием технических систем, характерно большое количество частных критериев эффективности, выраженных, как правило, нелинейными функциями, а иногда описанными алгоритмическими процедурами. Следовательно, поверхность эффективных точек Парето, на которой ищется наилучший вариант проекта, представляет собой сложный геометрический объект в n- мерном пространстве частных критериев. Выбор наилучшего решения предлагается путем использования предпочтения проектанта, сформулированными в виде функции ценности. Функция ценности также является сложной поверхностью в n-мерном критериальном пространстве. Аналитическое решение, дающее координаты точек касания этих поверхностей, представляет сложную математическую проблему. В статье предлагается численный метод решения задачи оптимизации по Парето для сложной технической системы, каковой является корабль. The article considers an approach to solving the problem of optimizing the ship's characteristics at the early stages of design. The optimization design problem is formulated as a multi-criteria one. A brief overview of approaches to solving mathematical programming problems of this class is given. We consider such a method of multi-criteria optimization, widely used in economic problems, as Pareto optimization. To solve problems related to the creation of technical systems, a large number of specific performance criteria are characteristic, expressed, as a rule, by nonlinear functions, and sometimes described by algorithmic procedures. Consequently, the surface of effective Pareto points, on which the best variant of the project is sought, is a complex geometric object in the n-dimensional space of partial criteria. The choice of the best solution is proposed by using the preferences of the designer, formulated in the form of a value function. The value function is also a complex surface in the n-dimensional criterion space. The analytical solution that gives the coordinates of the points of contact of these surfaces is a complex mathematical problem. The paper proposes a numerical method for solving the Pareto optimization problem for a complex technical system, such as a ship.
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Alghalith, Moawia. "New methods of modeling and estimating preferences." Studies in Economics and Finance 36, no. 1 (May 30, 2019): 83–88. http://dx.doi.org/10.1108/sef-12-2017-0354.
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Purpose This paper aims to quantify preferences without having to have any utility data. Design/methodology/approach We use duality theory, Taylor’s theorem and nonlinear regressions. Findings We presented pioneering quantitative methods in economics and business. These methods can be applied to numerous topics in empirical and theoretical economics and business. Moreover, this paper highlighted the interdisciplinary nature of economics. In doing so, it emphasized the interface between economics, marketing, management, statistics and mathematics. Furthermore, it circumvented a major obstacle in the literature: the curse of dimensionality. Originality/value The authors introduce a novel and convenient approach to utility modeling. In doing so, they present a general utility function in a simple form. Furthermore, they develop a method to measure preferences without any utility data. They also devise a method to measure the marginal utility. Then, they develop new methods of modeling and measuring the consumer utility. In so doing, they overcome a major obstacle: the curse of the dimensionality. In addition, they introduce new methods of modeling and measuring the consumer demand for the firm’s good.
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Zhang, Fan, Yuan Biao Zhang, Jian Zhang Yu, and Jian Bo Wang. "A Model for Priority Processing of Orders Based on Genetic Algorithm and Membership Function." Key Engineering Materials 439-440 (June 2010): 202–7. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.202.
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Order arrangement is always the headache thing for manufacturers even in this high-tech modern society. Hence, this paper manages to construct a nonlinear 0-1 programming mathematical model in search of optimal solution of minimizing the cost of default. And we make an attempt to apply Genetic Algorithm in accordance with this NP-hard problem. In designing the algorithm, we propose an encoding method based both on orders and working procedure, adopt Roulette Wheel method to select the next generation, and embed crossover and mutation to avoid the common defect of premature convergence. Noticing that firms would have various psychological preferences towards different orders, we introduce Membership Function to characterize the priority of orders by comprehensively concern processing complexity, urgency and revenue of orders.
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Posedel Šimović, Petra, and Azra Tafro. "Pricing the Volatility Risk Premium with a Discrete Stochastic Volatility Model." Mathematics 9, no. 17 (August 25, 2021): 2038. http://dx.doi.org/10.3390/math9172038.
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Investors’ decisions on capital markets depend on their anticipation and preferences about risk, and volatility is one of the most common measures of risk. This paper proposes a method of estimating the market price of volatility risk by incorporating both conditional heteroscedasticity and nonlinear effects in market returns, while accounting for asymmetric shocks. We develop a model that allows dynamic risk premiums for the underlying asset and for the volatility of the asset under the physical measure. Specifically, a nonlinear in mean time series model combining the asymmetric autoregressive conditional heteroscedastic model with leverage (NGARCH) is adapted for modeling return dynamics. The local risk-neutral valuation relationship is used to model investors’ preferences of volatility risk. The transition probabilities governing the evolution of the price of the underlying asset are adjusted for investors’ attitude towards risk, presenting the asset returns as a function of the risk premium. Numerical studies on asset return data show the significance of market shocks and levels of asymmetry in pricing the volatility risk. Estimated premiums could be used in option pricing models, turning options markets into volatility trading markets, and in measuring reactions to market shocks.
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Ounaies, Senda, Jean-Marc Bonnisseau, and Souhail Chebbi. "Equilibrium of a production economy with non-compact attainable allocations set." Advances in Nonlinear Analysis 8, no. 1 (June 30, 2018): 979–94. http://dx.doi.org/10.1515/anona-2017-0234.
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Abstract In this paper, we consider a production economy with an unbounded attainable set where the consumers may have non-complete non-transitive preferences. To get the existence of an equilibrium, we provide an asymptotic property on preferences for the attainable consumptions and we use a combination of the nonlinear optimization and fixed point theorems on truncated economies together with an asymptotic argument. We show that this condition holds true if the set of attainable allocations is compact or, when the preferences are representable by utility functions, if the set of attainable individually rational utility levels is compact. This assumption generalizes the CPP condition of [N. Allouch, An equilibrium existence result with short selling, J. Math. Econom. 37 2002, 2, 81–94] and covers the example of [F. H. Page, Jr., M. H. Wooders and P. K. Monteiro, Inconsequential arbitrage, J. Math. Econom. 34 2000, 4, 439–469] when the attainable utility levels set is not compact. So we extend the previous existence results with non-compact attainable sets in two ways by adding a production sector and considering general preferences.
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Bayada, G., M. El Alaoui Talibi, and C. Vázquez. "Existence of solutions for elastohydrodynamic piezoviscous lubrication problems with a new model of cavitation." European Journal of Applied Mathematics 7, no. 1 (February 1996): 63–73. http://dx.doi.org/10.1017/s0956792500002205.
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The purpose of this paper is to study a mathematical model of lubricating flow between elastic surfaces obeying the linear Hertzian theory when cavitation takes place. Cavitation is a free boundary phenomenon that is described in this paper by the New Elrod–Adams model. This model introduces the concentration of fluid as well as the pressure as unknown functions and is suggested in preference to the classical variational inequality due to its ability to describe inflow and outflow. This leads to a nonlinear variational and nonlocal equation. Herein, an existence theorem is proved by means of two different techniques.
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Green-Armytage, James. "Progressive and Regressive Equilibria in a Tax Competition Game." Public Finance Review 45, no. 3 (November 2, 2015): 307–33. http://dx.doi.org/10.1177/1091142115611740.
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This article models interjurisdictional competition over nonlinear taxes on the incomes of mobile individuals. Each individual has exogenous wealth and a location preference that is drawn from a continuous distribution. We find that more concave utility of consumption functions lead to more progressive tax structures, as richer people place less value on marginal consumption relative to location. In the benchmark model, a relative risk aversion coefficient of one is the boundary between progressivity and regressivity. The exercise helps us to understand which types of jurisdictions are more likely to have progressive taxes as their optimal policies.
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Rabiei, Kobra, Yadollah Ordokhani, and Esmaeil Babolian. "Fractional-order Boubaker functions and their applications in solving delay fractional optimal control problems." Journal of Vibration and Control 24, no. 15 (May 1, 2017): 3370–83. http://dx.doi.org/10.1177/1077546317705041.
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In this paper, a new set of functions called fractional-order Boubaker functions is defined for solving the delay fractional optimal control problems with a quadratic performance index. To solve the problem, first we obtain the operational matrix of the Caputo fractional derivative of these functions and the operational matrix of multiplication to solve the nonlinear problems for the first time. Also, a general formulation for the delay operational matrix of these functions has been achieved. Then we utilized these matrices to solve delay fractional optimal control problems directly. In fact, the delay fractional optimal control problem converts to an optimization problem, which can then be easily solved with the aid of the Gauss–Legendre integration formula and Newton’s iterative method. Convergence of the algorithm is proved. The applicability of the method is shown by some examples; moreover, a comparison with the existing results shows the preference of this method.
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Suwal, Bhim Raj, Jane Zhao, Aditi Raina, Xun Wu, Namrata Chindarkar, K. C. Bal Kumar, and Dale Whittington. "Households' preferences for water tariff structures in Kathmandu, Nepal." Water Policy 21, S1 (August 26, 2019): 9–28. http://dx.doi.org/10.2166/wp.2019.079.
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Abstract Despite being politically sensitive, water tariffs are frequently administered without information about households' preferences for tariff structures. In this paper we examine the tariff preferences of 1,500 households in Kathmandu, Nepal. We first use a bivariate probit model to examine stated preferences for (1) an increasing block tariff (IBT) and (2) a positive fixed charge. We find that household preferences for IBTs and fixed charges are not easily explained by household socioeconomic and water use characteristics. Second, we ask respondents what they think a fair water bill would be for a randomly assigned quantity of water. We model the responses as a function of both quantity and household socioeconomic and water use characteristics. While households support a water tariff that results in a household's water bill increasing as a household's water use increases, we do not find evidence that households support an increasing, nonlinear relationship between water use and a household's water bill. Our results suggest that respondents desire affordable piped water services and water bills that are calculated fairly for everyone. Because the notion of fairness in Kathmandu varies, utility managers may have considerable latitude in choosing a tariff structure that focuses on other objectives, such as cost recovery, revenue stability, and economic efficiency.
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Wei, Quan, and Fang Jiancheng. "Research on FKF Method Based on an Improved Genetic Algorithm for Multi-sensor Integrated Navigation System." Journal of Navigation 65, no. 3 (March 23, 2012): 495–511. http://dx.doi.org/10.1017/s0373463312000094.
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The fusion of multi-sensor data can provide more accurate and reliable navigation performance than single-sensor methods. However, the general Federated Kalman Filter (FKF) is not suitable for large changes of complex nonlinear systems parameters and is not optimized for effective information sharing coefficients to estimate navigation preferences. This study concerns research on the FKF method for a nonlinear adaptive model based on an improved Genetic Algorithm (GA) for the Strapdown Inertial Navigation System (SINS) / Celestial Navigation System (CNS) / Global Positioning System (GPS) integrated multi-sensor navigation system. An improved fitness function avoids the premature convergence problem of a general GA and decimal coding improves its performance. The improved GA is used to build the adaptive FKF model and to select the optimized information sharing coefficients of the FKF. An Unscented Kalman Filter (UKF) is used to deal with the nonlinearity of integrated navigation system. Finally, a solution and implementation of the system is proposed and verified experimentally.
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Chernozhukov, Victor, Whitney K. Newey, and Rahul Singh. "Automatic Debiased Machine Learning of Causal and Structural Effects." Econometrica 90, no. 3 (2022): 967–1027. http://dx.doi.org/10.3982/ecta18515.
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Many causal and structural effects depend on regressions. Examples include policy effects, average derivatives, regression decompositions, average treatment effects, causal mediation, and parameters of economic structural models. The regressions may be high‐dimensional, making machine learning useful. Plugging machine learners into identifying equations can lead to poor inference due to bias from regularization and/or model selection. This paper gives automatic debiasing for linear and nonlinear functions of regressions. The debiasing is automatic in using Lasso and the function of interest without the full form of the bias correction. The debiasing can be applied to any regression learner, including neural nets, random forests, Lasso, boosting, and other high‐dimensional methods. In addition to providing the bias correction, we give standard errors that are robust to misspecification, convergence rates for the bias correction, and primitive conditions for asymptotic inference for estimators of a variety of estimators of structural and causal effects. The automatic debiased machine learning is used to estimate the average treatment effect on the treated for the NSW job training data and to estimate demand elasticities from Nielsen scanner data while allowing preferences to be correlated with prices and income.