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Journal articles on the topic 'Nonlinear adaptive models'

Author: Grafiati

Published: 6 September 2023

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1

Shapiro, Arnold F., and R. Paul Gorman. "Implementing adaptive nonlinear models." Insurance: Mathematics and Economics 26, no. 2-3 (May 2000): 289–307. http://dx.doi.org/10.1016/s0167-6687(00)00036-6.

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2

Priscoli, F. Delli, L. Marconi, and A. Isidori. "Adaptive observers as nonlinear internal models." Systems & Control Letters 55, no. 8 (August 2006): 640–49. http://dx.doi.org/10.1016/j.sysconle.2005.09.016.

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3

Ye, Xudong. "Nonlinear adaptive control using multiple identification models." Systems & Control Letters 57, no. 7 (July 2008): 578–84. http://dx.doi.org/10.1016/j.sysconle.2007.12.007.

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4

Coad, D. S., and M. B. Woodroofe. "Corrected confidence intervals for adaptive nonlinear regression models." Journal of Statistical Planning and Inference 130, no. 1-2 (March 2005): 63–83. http://dx.doi.org/10.1016/j.jspi.2004.02.020.

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5

Murray-Smith, Roderick, and Daniel Sbarbaro. "NONLINEAR ADAPTIVE CONTROL USING NONPARAMETRIC GAUSSIAN PROCESS PRIOR MODELS." IFAC Proceedings Volumes 35, no. 1 (2002): 325–30. http://dx.doi.org/10.3182/20020721-6-es-1901.01040.

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6

Fink, Alexander, Martin Fischer, and Oliver Nelles. "Supervision of nonlinear adaptive controllers based on fuzzy models." IFAC Proceedings Volumes 32, no. 2 (July 1999): 8602–7. http://dx.doi.org/10.1016/s1474-6670(17)57467-4.

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7

Chen, Lingji, and Kumpati S. Narendra. "Nonlinear adaptive control using neural networks and multiple models." Automatica 37, no. 8 (August 2001): 1245–55. http://dx.doi.org/10.1016/s0005-1098(01)00072-3.

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8

McLain, Richard B., and Michael A. Henson. "Nonlinear Model Reference Adaptive Control with Embedded Linear Models." Industrial & Engineering Chemistry Research 39, no. 8 (August 2000): 3007–17. http://dx.doi.org/10.1021/ie990088t.

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9

Fink, Alexander, Martin Fischer, Oliver Nelles, and Rolf Isermann. "Supervision of nonlinear adaptive controllers based on fuzzy models." Control Engineering Practice 8, no. 10 (October 2000): 1093–105. http://dx.doi.org/10.1016/s0967-0661(00)00059-9.

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10

Ma, Zixiao, Zhaoyu Wang, Yifei Guo, Yuxuan Yuan, and Hao Chen. "Nonlinear Multiple Models Adaptive Secondary Voltage Control of Microgrids." IEEE Transactions on Smart Grid 12, no. 1 (January 2021): 227–38. http://dx.doi.org/10.1109/tsg.2020.3023307.

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11

Moussas, Vassilios C., Sokratis K. Katsikas, and Demetrios G. Lainiotis. "Adaptive Estimation of FCG Using Nonlinear State-Space Models." Stochastic Analysis and Applications 23, no. 4 (July 2005): 705–22. http://dx.doi.org/10.1081/sap-200064462.

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12

Annaswamy, A. M., C. Thanomsat, N. Mehta, and Ai-Poh Loh. "Applications of Adaptive Controllers to Systems With Nonlinear Parametrization." Journal of Dynamic Systems, Measurement, and Control 120, no. 4 (December 1, 1998): 477–87. http://dx.doi.org/10.1115/1.2801489.

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Nonlinear parametrizations occur in dynamic models of several complex engineering problems. The theory of adaptive estimation and control has been applicable, by and large, to problems where parameters appear linearly. We have recently developed an adaptive controller that is capable of estimating parameters that appear nonlinearly in dynamic systems in a stable manner. In this paper, we present this algorithm and its applicability to two problems, temperature regulation in chemical reactors and precise positioning using magnetic bearings both of which contain nonlinear parametrizations. It is shown in both problems that the proposed controller leads to a significantly better performance than those based on linear parametrizations or linearized dynamics.

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13

Ke, Haisen, and Jiang Li. "Adaptive Control for a Class of Nonlinear System with Redistributed Models." Journal of Control Science and Engineering 2012 (2012): 1–6. http://dx.doi.org/10.1155/2012/409139.

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Multiple model adaptive control has been investigated extensively during the last ten years in which the “switching” or “switching and tuning” have emerged as the mainly approaches. It is the “switching” that can improve the transient performance to some extent and also make it difficult to analyze the stability of the system with multiple models adaptive controller. Towards this goal, this paper develops a novel multiple models adaptive controller for a class of nonlinear system in parameter-strict-feedback form which not only improves the transient performance significantly, but also guarantees the stability of all the states of the closed-loop system. A simulation example is proposed to illustrate the effectiveness of the developed multiple models adaptive controller.

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14

Chikkula, Yugender, and Jay H. Lee. "Robust Adaptive Predictive Control of Nonlinear Processes Using Nonlinear Moving Average System Models." Industrial & Engineering Chemistry Research 39, no. 6 (June 2000): 2010–23. http://dx.doi.org/10.1021/ie990393e.

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15

Befigiannis, G. N., E. N. Demiris, and S. D. Likothanassis. "Evolutionary Nonlinear Multimodel Partitioning Filters." Journal of Advanced Computational Intelligence and Intelligent Informatics 5, no. 1 (January 20, 2001): 8–14. http://dx.doi.org/10.20965/jaciii.2001.p0008.

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The problem of designing adaptive filters for nonlinear systems is faced in this work. The proposed evolution program combines the effectiveness of multimodel adaptive filters and the robustness of genetic algorithms (GAs). Specifically, a bank of different extended Kalman filters is implemented. Then, the a posteriori probability that a specific model of the bank of conditional models is the true one can be used as a GA fitness function. The superiority of the algorithm is that it evolves concurrently the models’ population with initial conditions. Thus, this procedure alleviates extended Kalman filter sensitivity in initial conditions, by estimating the best values. In addition to this, adaptive implementation is proposed that relieves the disadvantage of time-consuming GA implementation. Finally, a variety of defined crossover and mutation operators is investigated in order to accelerate the algorithm’s convergence.

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16

Kürten, Karl E. "Adaptive architectures for Hebbian network models." Journal de Physique I 2, no. 5 (May 1992): 615–24. http://dx.doi.org/10.1051/jp1:1992105.

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17

Branch, William A., Troy Davig, and Bruce McGough. "ADAPTIVE LEARNING IN REGIME-SWITCHING MODELS." Macroeconomic Dynamics 17, no. 5 (March 6, 2012): 998–1022. http://dx.doi.org/10.1017/s1365100511000800.

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We study adaptive learning in economic environments subject to recurring structural change. Stochastically evolving institutional and policymaking features can be described by regime-switching models with parameters that evolve according to finite state Markov processes. We demonstrate that in nonlinear models of this form, the presence of sunspot equilibria implies two natural schemes for learning the conditional means of endogenous variables: under mean value learning, agents condition on a sunspot variable that captures the self-fulfilling serial correlation in the equilibrium, whereas under vector autoregression learning (VAR learning), the self-fulfilling serial correlation must be learned. We show that an intuitive condition ensures convergence to a regime-switching rational expectations equilibrium. However, the stability of sunspot equilibria, when they exist, depends on whether agents adopt mean value or VAR learning: coordinating on sunspot equilibria via a VAR learning rule is not possible. To illustrate these phenomena, we develop results for an overlapping-generations model and a New Keynesian model.

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18

Bando, Mai, and Akira Ichikawa. "Adaptive Regulation of Nonlinear Systems by Output Feedback." Journal of Robotics and Mechatronics 20, no. 5 (October 20, 2008): 719–25. http://dx.doi.org/10.20965/jrm.2008.p0719.

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In this paper adaptive regulation by output feedback is considered for a class of single-input/single-output nonlinear systems described by multiple linear models. The adaptive laws are based on the filtered state and input of an adaptive observer. Then a controller is given by a state-dependent Riccati equation, which assures the stability of the adaptive system. Simulation results are given to illustrate the theory.

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19

El Hamidi, Khadija, Mostafa Mjahed, Abdeljalil El Kari, and Hassan Ayad. "Adaptive Control Using Neural Networks and Approximate Models for Nonlinear Dynamic Systems." Modelling and Simulation in Engineering 2020 (August 26, 2020): 1–13. http://dx.doi.org/10.1155/2020/8642915.

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In this research, a comparative study of two recurrent neural networks, nonlinear autoregressive with exogenous input (NARX) neural network and nonlinear autoregressive moving average (NARMA-L2), and a feedforward neural network (FFNN) is performed for their ability to provide adaptive control of nonlinear systems. Three dynamical nonlinear systems of different complexity are considered. The aim of this work is to make the output of the plant follow the desired reference trajectory. The problem becomes more challenging when the dynamics of the plants are assumed to be unknown, and to tackle this problem, a multilayer neural network-based approximate model is set up which will work in parallel to the plant and the control scheme. The network parameters are updated using the dynamic backpropagation (BP) algorithm.

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20

Satpathy, Anurag, Ganapati Panda, Rajasekhar Gogula, and Renu Sharma. "Low Complexity Adaptive Nonlinear Models for the Diagnosis of Periodontal Disease." International Journal of Sensors, Wireless Communications and Control 10, no. 4 (December 18, 2020): 508–21. http://dx.doi.org/10.2174/2210327909666191211125358.

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Background / Objective: The paper addresses a specific clinical problem of diagnosis of periodontal disease with an objective to develop and evaluate the performance of low complexity Adaptive Nonlinear Models (ANM) using nonlinear expansion schemes and describes the basic structure and development of ANMs in detail. Methods: Diagnostic data pertaining to periodontal findings of teeth obtained from patients have been used as inputs to train and validate the proposed models. Results: Results obtained from simulations experiments carried out using various nonlinear expansion schemes have been compared in terms of various performance measures such as Mean Absolute Percentage Error (MAPE), matching efficiency, sensitivity, specificity, false positive rate, false negative rate and diagnostic accuracy. Conclusion: The ANM with seven trigonometric expansion scheme demonstrates the best performance in terms of all measures yielding a diagnostic accuracy of 99.11% compared to 94.64% provided by adaptive linear model.

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21

Phillips, R. F. "Partially adaptive estimation of nonlinear models via a normal mixture." Econometric Reviews 18, no. 2 (January 1999): 141–67. http://dx.doi.org/10.1080/07474939908800437.

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22

Roop, John Paul. "Numerical comparison of nonlinear subgridscale models via adaptive mesh refinement." Mathematical and Computer Modelling 46, no. 11-12 (December 2007): 1487–506. http://dx.doi.org/10.1016/j.mcm.2006.02.023.

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23

Shapiro, Arnold F. "A Hitchhiker’s guide to the techniques of adaptive nonlinear models." Insurance: Mathematics and Economics 26, no. 2-3 (May 2000): 119–32. http://dx.doi.org/10.1016/s0167-6687(99)00058-x.

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24

Sofianos, Nikolaos A., and Yiannis S. Boutalis. "Robust adaptive multiple models based fuzzy control of nonlinear systems." Neurocomputing 173 (January 2016): 1733–42. http://dx.doi.org/10.1016/j.neucom.2015.09.047.

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25

Li, Xiao-Li, Chao Jia, De-Xin Liu, and Da-Wei Ding. "Nonlinear adaptive control using multiple models and dynamic neural networks." Neurocomputing 136 (July 2014): 190–200. http://dx.doi.org/10.1016/j.neucom.2014.01.013.

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26

Slotine, J. J. E., and Β. E. Ydstie. "Nonlinear Process Control: An Adaptive Approach which Uses Physical Models." IFAC Proceedings Volumes 22, no. 3 (June 1989): 357–62. http://dx.doi.org/10.1016/s1474-6670(17)53661-7.

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27

He, Wanli, Philip Avery, and Charbel Farhat. "In situ adaptive reduction of nonlinear multiscale structural dynamics models." International Journal for Numerical Methods in Engineering 121, no. 22 (August 16, 2020): 4971–88. http://dx.doi.org/10.1002/nme.6505.

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28

Fu, Yue, and Tianyou Chai. "Nonlinear multivariable adaptive control using multiple models and neural networks." Automatica 43, no. 6 (June 2007): 1101–10. http://dx.doi.org/10.1016/j.automatica.2006.12.010.

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29

Zhou, Jun, Zhenzhen Ge, and Jianguo Guo. "A Novel Adaptive Control Method Based on Continuous Characteristic Models." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 4 (August 2018): 603–10. http://dx.doi.org/10.1051/jnwpu/20183640603.

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For a class of nonlinear systems, a novel adaptive control scheme is proposed in this paper. Firstly, the continuous characteristic model is constructed, which is equivalent to its original system in output for the same input at any time. Thus, the original system can be replaced with its characteristic model for designing controllers. As the original nonlinear system has unknown parameters and unmodeled dynamics, the characteristic parameters are also unknown and fast time-varying. A novel adaptive control method is proposed by combining the continuous characteristic model with the adaptive dynamic surface control technique. The method is independent on the original system model and only utilizes the information about system output for controlling. On account of the fact, the system inner states, unknown parameters and unmodeled dynamics are not needed to be observed. Finally, the stability of the closed loop system is proved by utilizing Lyapunov theory and simulation results have demonstrated the effectiveness of the proposed method.

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30

Comte, F., J. Dedecker, and M. L. Taupin. "ADAPTIVE DENSITY ESTIMATION FOR GENERAL ARCH MODELS." Econometric Theory 24, no. 6 (July 17, 2008): 1628–62. http://dx.doi.org/10.1017/s026646660808064x.

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We consider a model Yt = σtηt in which (σt) is not independent of the noise process (ηt) but σt is independent of ηt for each t. We assume that (σt) is stationary, and we propose an adaptive estimator of the density of ln(σt2) based on the observations Yt. Under a new dependence structure, the τ-dependency defined by Dedecker and Prieur (2005, Probability Theory and Related Fields 132, 203–236), we prove that the rates of this nonparametric estimator coincide with the rates obtained in the independent and identically distributed (i.i.d.) case when (σt) and (ηt) are independent. The results apply to various linear and nonlinear general autoregressive conditionally heteroskedastic (ARCH) processes. They are illustrated by simulations applying the deconvolution algorithm of Comte, Rozenholc, and Taupin (2006, Canadian Journal of Statistics 34, 431–452) to a new noise density.

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31

Heid, Pascal, and Thomas P. Wihler. "A modified Kačanov iteration scheme with application to quasilinear diffusion models." ESAIM: Mathematical Modelling and Numerical Analysis 56, no. 2 (February 18, 2022): 433–50. http://dx.doi.org/10.1051/m2an/2022008.

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The classical Kačanov scheme for the solution of nonlinear variational problems can be interpreted as a fixed point iteration method that updates a given approximation by solving a linear problem in each step. Based on this observation, we introduce a modified Kačanov method, which allows for (adaptive) damping, and, thereby, to derive a new convergence analysis under more general assumptions and for a wider range of applications. For instance, in the specific context of quasilinear diffusion models, our new approach does no longer require a standard monotonicity condition on the nonlinear diffusion coefficient to hold. Moreover, we propose two different adaptive strategies for the practical selection of the damping parameters involved.

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32

Freise, Fritjof, Norbert Gaffke, and Rainer Schwabe. "Convergence of least squares estimators in the adaptive Wynn algorithm for some classes of nonlinear regression models." Metrika 84, no. 6 (February 8, 2021): 851–74. http://dx.doi.org/10.1007/s00184-020-00803-0.

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AbstractThe paper continues the authors’ work (Freise et al. The adaptive Wynn-algorithm in generalized linear models with univariate response. arXiv:1907.02708, 2019) on the adaptive Wynn algorithm in a nonlinear regression model. In the present paper the asymptotics of adaptive least squares estimators under the adaptive Wynn algorithm is studied. Strong consistency and asymptotic normality are derived for two classes of nonlinear models: firstly, for the class of models satisfying a condition of ‘saturated identifiability’, which was introduced by Pronzato (Metrika 71:219–238, 2010); secondly, a class of generalized linear models. Further essential assumptions are compactness of the experimental region and of the parameter space together with some natural continuity assumptions. For asymptotic normality some further smoothness assumptions and asymptotic homoscedasticity of random errors are needed and the true parameter point is required to be an interior point of the parameter space.

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33

Prawin, J., and A. Rama Mohan Rao. "Nonlinear Structural Damage Detection Based on Adaptive Volterra Filter Model." International Journal of Structural Stability and Dynamics 18, no. 02 (February 2018): 1871003. http://dx.doi.org/10.1142/s0219455418710037.

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The majority of the existing damage diagnostic techniques are based on linear models. Changes in the state of the dynamics of these models, before and after damage in the structure based on the vibration measurements, are popularly used as damage indicators. However, the system may initially behave linearly and subsequently exhibit nonlinearity due to the incipience of damage. Breathing cracks that exhibit bilinear behavior are one such example of the damage induced due to nonlinearity. Further many real world structures even in their undamaged state are nonlinear. Hence, in this paper, we present a nonlinear damage detection technique based on the adaptive Volterra filter using the nonlinear time history response. Three damage indices based on the adaptive Volterra filter are proposed and their sensitiveness to damage and noise is assessed through two numerically simulated examples. Numerical investigations demonstrate the effectiveness of the adaptive Volterra filter model to detect damage in nonlinear structures even with measurement noise.

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34

Li, Xiao-Li, De-Xin Liu, Jiang-Yun Li, and Da-Wei Ding. "Robust Adaptive Control for Nonlinear Discrete-Time Systems by Using Multiple Models." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/679039.

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Back propagation (BP) neural network is used to approximate the dynamic character of nonlinear discrete-time system. Considering the unmodeling dynamics of the system, the weights of neural network are updated by using a dead-zone algorithm and a robust adaptive controller based on the BP neural network is proposed. For the situation that jumping change parameters exist, multiple neural networks with multiple weights are built to cover the uncertainty of parameters, and multiple controllers based on these models are set up. At every sample time, a performance index function based on the identification error will be used to choose the optimal model and the corresponding controller. Different kinds of combinations of fixed model and adaptive model will be used for robust multiple models adaptive control (MMAC). The proof of stability and convergence of MMAC are given, and the significant efficacy of the proposed methods is tested by simulation.

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35

Bi, Wenjie, Yinghui Sun, Haiying Liu, and Xiaohong Chen. "Dynamic Nonlinear Pricing Model Based on Adaptive and Sophisticated Learning." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/791656.

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Existing dynamic pricing models which take consumers’ learning behavior into account generally assume that consumers learn on the basis of reinforcement learning and belief-based learning. Nevertheless, abundant empirical evidence of behavior game indicates that consumers’ learning is normally described as a process of mixed learning. Particularly, for experience goods, a consumer’s purchase decision is not only based on his previous purchase behavior (adaptive learning), but also affected by that of other consumers (sophisticated learning). With the assumption that consumers are both adaptive and sophisticated learners, we study a dynamic pricing model dealing with repeated decision problems in a duopoly market. Specifically, we build a dynamic game model based on sophisticated experience-weighted attraction learning model (SEWA) and analyze the existence of the equilibrium. Finally, we show the characteristics and differences of the steady-state solutions between models considering adaptive consumers and models considering sophistical consumers by numerical results.

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36

Bando, Mai, and Akira Ichikawa. "ADAPTIVE OUTPUT REGULATION OF NONLINEAR SYSTEMS DESCRIBED BY MULTIPLE LINEAR MODELS." IFAC Proceedings Volumes 40, no. 13 (2007): 269–74. http://dx.doi.org/10.3182/20070829-3-ru-4911.00044.

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37

Yusheng Liu and Xing-Yuan Li. "Robust adaptive control of nonlinear systems represented by input~output models." IEEE Transactions on Automatic Control 48, no. 6 (June 2003): 1041–45. http://dx.doi.org/10.1109/tac.2003.812797.

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38

Zong, Xiao Ping, Miao Zhang, and Pei Guang Wang. "Adaptive Controller Design for SISO Switched Nonlinear Systems with Linear Uncertain Parameters." Applied Mechanics and Materials 602-605 (August 2014): 1362–66. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.1362.

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This paper presents that single input single output (SISO) switched nonlinear system tracks the variation of the state error to approach the excepted values by using model reference adaptive control (MRAC) method. In order to improve the adaptive control for nonlinear systems by Using Narendra method and dividing the system into two parts: linear and nonlinear parts. The controllers are designed to guarantee that the systems are closed to the model reference system with the arbitrary switching signal. Switching systems can ensure choose the best controller so that can enhance the performance. The adaptive laws are given that are based on a class of feedback single input single output nonlinear uncertain systems which can switch feedback linear standard models. The adaptive laws are different from the classic adaptive laws, because they vary with different switching signals until the best matching one comes. Simulation results show that the proposed method is effective.

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39

Tie, Yu Jia, Wei Yang, and Hao Yu Tan. "Spacecraft Attitude and Orbit Coupled Nonlinear Adaptive Synchronization Control." Advanced Materials Research 327 (September 2011): 6–11. http://dx.doi.org/10.4028/www.scientific.net/amr.327.6.

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Precise dynamic model of spacecraft is essential for the space missions, to be completed successfully. Nevertheless, the independent orbit or attitude dynamic models can not meet high precision tasks. This paper developed a 6-DOF relative coupling dynamic model based upon the nonlinear relative motion dynamics equations and attitude kinematics equations described by MRP. Nonlinear synchronization control law was designed for the coupled nonlinear dynamic model, whose close-loop system was proved to be global asymptotic stable by Lyapunov direct method. Finallly, the simulation results illustrate that the nonlinear adaptive synchronization control algorithm can robustly drive the orbit and attitude errors to converge to zero.

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40

Costa, Antonio C., Tosif Ahamed, and Greg J. Stephens. "Adaptive, locally linear models of complex dynamics." Proceedings of the National Academy of Sciences 116, no. 5 (January 17, 2019): 1501–10. http://dx.doi.org/10.1073/pnas.1813476116.

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The dynamics of complex systems generally include high-dimensional, nonstationary, and nonlinear behavior, all of which pose fundamental challenges to quantitative understanding. To address these difficulties, we detail an approach based on local linear models within windows determined adaptively from data. While the dynamics within each window are simple, consisting of exponential decay, growth, and oscillations, the collection of local parameters across all windows provides a principled characterization of the full time series. To explore the resulting model space, we develop a likelihood-based hierarchical clustering, and we examine the eigenvalues of the linear dynamics. We demonstrate our analysis with the Lorenz system undergoing stable spiral dynamics and in the standard chaotic regime. Applied to the posture dynamics of the nematode Caenorhabditis elegans, our approach identifies fine-grained behavioral states and model dynamics which fluctuate about an instability boundary, and we detail a bifurcation in a transition from forward to backward crawling. We analyze whole-brain imaging in C. elegans and show that global brain dynamics is damped away from the instability boundary by a decrease in oxygen concentration. We provide additional evidence for such near-critical dynamics from the analysis of electrocorticography in monkey and the imaging of a neural population from mouse visual cortex at single-cell resolution.

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41

Liu, Ning, Yu Sheng Liu, and Qiang Yang. "Robust Adaptive Control of Nonlinear Systems with Uncertainties." Applied Mechanics and Materials 568-570 (June 2014): 1108–12. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.1108.

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This paper proposes a robust adaptive robust controller for nonlinear systems represented by input-output models with unmodeled dynamics. Under the circumstances that the output of the system is bounded, the proposed controller can guarantee that all the variables of the system are bounded in the presence of unmodeled dynamics and time-varying disturbances. The scheme does not need to generate an additional dynamic signal to dominate the effects of the unmodeled dynamics. It is shown that the mean-square tracking error can be made arbitrarily small by choosing some design parameters appropriately.

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42

Rehman, Khawar, Yung-Chieh Wang, Muhammad Waseem, and Seung Ho Hong. "Tree-based machine learning models for prediction of bed elevation around bridge piers." Physics of Fluids 34, no. 8 (August 2022): 085105. http://dx.doi.org/10.1063/5.0098394.

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Scouring around bridge piers is a highly nonlinear process making its prediction by deterministic and stochastic models challenging. This study explores the application of inferential models for predictions of bed elevations around bridge piers. The objective is to get a generalized machine learning model with an interpretable structure. The historical data comprise a detailed record of streamflow and bed elevations that were captured by sensors installed at the 5th Street Bridge piers over Ocmulgee River at Macon, GA. We investigate the accuracy and efficiency of various tree-based machine learning algorithms, including a single tree as well as homogeneous ensemble models for simultaneous predictions of bed elevation at multiple sensors installed at piers. The ensemble models were based on bagging and boosting techniques. Special attention is given to balancing between overfitting and underfitting without compromise on the model's robustness. Observation of the performance metrics showed that tree-based models have excellent predictive capacity. It was observed that boosting models, including a gradient based regression model, and adaptive boosting outperformed the bagging model. Among all the models investigated in this study, the adaptive boosting method was observed to be most generalizable. The performance of developed models shows the potential of tree-based ensemble models in providing rapid and robust predictions for complex nonlinear fluid flows.

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43

Fiori, Simone. "Closed-Form Expressions of Some Stochastic Adapting Equations for Nonlinear Adaptive Activation Function Neurons." Neural Computation 15, no. 12 (December 1, 2003): 2909–29. http://dx.doi.org/10.1162/089976603322518795.

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In recent work, we introduced nonlinear adaptive activation function (FAN) artificial neuron models, which learn their activation functions in an unsupervised way by information-theoretic adapting rules. We also applied networks of these neurons to some blind signal processing problems, such as independent component analysis and blind deconvolution. The aim of this letter is to study some fundamental aspects of FAN units' learning by investigating the properties of the associated learning differential equation systems.

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44

Siek, M., and D. P. Solomatine. "Nonlinear chaotic model for predicting storm surges." Nonlinear Processes in Geophysics 17, no. 5 (September 6, 2010): 405–20. http://dx.doi.org/10.5194/npg-17-405-2010.

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Abstract. This paper addresses the use of the methods of nonlinear dynamics and chaos theory for building a predictive chaotic model from time series. The chaotic model predictions are made by the adaptive local models based on the dynamical neighbors found in the reconstructed phase space of the observables. We implemented the univariate and multivariate chaotic models with direct and multi-steps prediction techniques and optimized these models using an exhaustive search method. The built models were tested for predicting storm surge dynamics for different stormy conditions in the North Sea, and are compared to neural network models. The results show that the chaotic models can generally provide reliable and accurate short-term storm surge predictions.

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45

Dukkipati, Rao V., and Satya S. Vallurupalli. "ADAPTIVE CONTROL OF AN ACTIVE SUSPENSION FOR NONLINEAR TIME VARYING VEHICLE PLANT." Transactions of the Canadian Society for Mechanical Engineering 24, no. 3-4 (September 2000): 525–46. http://dx.doi.org/10.1139/tcsme-2000-0039.

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This paper presents a new adaptive control approach to general multi-degrees-of-freedom suspension models. The control concept diverts from the widely applied optimal control to adaptive control. The basic idea involves obtaining optimal performance of any nonlinear time varying suspension model by adaptively following a predefined reference model. Optimal performance is achieved by an adaptive control law, which involves feed forward, feedback and auxiliary controller parameters. Model reference adaptive control is used to derive adaptation laws for the controller. The proposed control scheme is computationally fast and does not require a priori knowledge of complex nonlinear dynamic variations and time varying parameters of the model. Simulation results for a two-degree of freedom nonlinear suspension model subjected to random asphalt road input are presented. The time and frequency domain results indicate good performance of adaptive controller even for large dynamic variations of model.

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Chen, An-Sing, and Mark T. Leung. "Dynamic Foreign Currency Trading Guided by Adaptive Forecasting." Review of Pacific Basin Financial Markets and Policies 01, no. 03 (September 1998): 383–418. http://dx.doi.org/10.1142/s0219091598000247.

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The difficulty in predicting exchange rates has been a long-standing problem in international finance as most standard econometric methods are unable to produce significantly better forecasts than the random walk model. Recent studies provide some evidence for the ability of multivariate time-series models to generate better forecasts. At the same time, artificial neural network models have been emerging as alternatives to predict exchange rates. In this paper we propose a nonlinear forecast model combining the neural network with the multivariate econometric framework. This hybrid model contains two forecasting stages. A time series approach based on Bayesian Vector Autoregression (BVAR) models is applied to the first stage of forecasting. The estimates from BVAR are then used by the nonparametric General Regression Neural Network (GRNN) to generate enhanced forecasts. To evaluate the economic impact of forecasts, we develop a set of currency trading rules guided by these models. The optimal conditions implied by the investment rules maximize the expected profits given the expected changes in exchange rates and the interest rate differentials between domestic and foreign countries. Both empirical and simulation experiments suggest that the proposed nonlinear adaptive forecasting model not only produces better forecasts but also results in higher investment returns than other types of models. The effect of risk aversion is also considered in the investment simulation.

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Bercu, B., and B. Portier. "Adaptive control of parametric nonlinear autoregressive models via a new martingale approach." IEEE Transactions on Automatic Control 47, no. 9 (September 2002): 1524–28. http://dx.doi.org/10.1109/tac.2002.802756.

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Khalil, H. K. "Adaptive output feedback control of nonlinear systems represented by input-output models." IEEE Transactions on Automatic Control 41, no. 2 (1996): 177–88. http://dx.doi.org/10.1109/9.481517.

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Tenan, Matthew S., Timothy Mauntel, and Jonathan Dickens. "Nonlinear Models And Computer Adaptive Testing Can Decrease Orthopedic Patient Survey Burden." Medicine & Science in Sports & Exercise 52, no. 7S (July 2020): 398. http://dx.doi.org/10.1249/01.mss.0000678168.37544.37.

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Kan, Rui. "Adaptive switching control of discrete time nonlinear systems based on multiple models." Journal of Control Theory and Applications 2, no. 1 (February 2004): 43–50. http://dx.doi.org/10.1007/s11768-004-0022-x.

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