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Journal articles on the topic 'Linear Performance Model'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Yakimoff, Naum, Stefan Mateeff, Walter H. Ehrenstein, and Joachim Hohnsbein. "Motion Extrapolation Performance: A Linear Model Approach." Human Factors: The Journal of the Human Factors and Ergonomics Society 35, no. 3 (September 1993): 501–10. http://dx.doi.org/10.1177/001872089303500307.

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2

Rasoava, Rijamampianina. "Executive compensation and firm performance: a non-linear relationship." Problems and Perspectives in Management 17, no. 2 (April 16, 2019): 1–17. http://dx.doi.org/10.21511/ppm.17(2).2019.01.

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In order to ensure profitability for shareholders, optimal contracting recommends the alignment between executive compensation and company performance. Large organizations have therefore adopted executives remuneration systems in order to induce positive market reaction and motivate executives. Complex compensation schemes are designed by Boards of Directors using strong pay-performance incentives that explain high levels of executive pay along with company size, demand for management skills and executive influence. However, the literature remains inconclusive on the pay-performance relationship owing to the various empirical methods used by researchers. Additionally, there has been little effort in the literature to compare methodologies on the pay-performance relationship. Using the dominant agency theory framework, the purpose of this study is to establish and examine the relationship between firm performance and executive pay. In addition, it intends to assess the characteristic of model specifications commonly adopted. To this aim, a quantitative analysis consisting of three complementary methods was performed on panel data from South African listed companies. The results of the main unrestricted first difference model indicate a strong non-linear relationship where the impact of current and previous firm performance on executive pay can be observed over 2 to 4-year period providing support to the optimal contracting theoretical perspective in the South African business context. In addition, CEO pay is more sensitive to firm performance as compared to Director pay. Lastly, although it affects executive pay levels, company size is not found to improve the pay-performance relationship.

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3

Johnson, Richard F. "A Log-Linear Model of Sentry Duty Performance." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 39, no. 14 (October 1995): 963. http://dx.doi.org/10.1177/154193129503901465.

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4

El Korso, Mohammed Nabil, Remy Boyer, Pascal Larzabal, and Bernard-Henri Fleury. "Estimation Performance for the Bayesian Hierarchical Linear Model." IEEE Signal Processing Letters 23, no. 4 (April 2016): 488–92. http://dx.doi.org/10.1109/lsp.2016.2528579.

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5

Saneie, Hamid, Zahra Nasiri-Gheidari, and Farid Tootoonchian. "Analytical model for performance prediction of linear resolver." IET Electric Power Applications 11, no. 8 (September 1, 2017): 1457–65. http://dx.doi.org/10.1049/iet-epa.2016.0693.

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6

Nevill, Alan M., and John B. Copas. "Using Generalized Linear Models (GLMs) to Model Errors in Motor Performance." Journal of Motor Behavior 23, no. 4 (December 1991): 241–50. http://dx.doi.org/10.1080/00222895.1991.9942035.

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7

Li, Yan-Jang, and Szu-Chi Tien. "Linear Model-based Feedforward Control for Improving Tracking-performance of Linear Motors." Asian Journal of Control 16, no. 6 (February 18, 2014): 1602–11. http://dx.doi.org/10.1002/asjc.842.

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8

Alberto, Chapa Martell Mario, and Hiroyuki Sato. "Linear Performance-Breakdown Model: A Framework for GPU kernel programs performance analysis." International Journal of Networking and Computing 5, no. 1 (2015): 86–104. http://dx.doi.org/10.15803/ijnc.5.1_86.

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9

Bivin, David G. "Gauging the performance of the linear-quadratic inventory model." Applied Economics 37, no. 11 (June 20, 2005): 1215–31. http://dx.doi.org/10.1080/00036840500118317.

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10

Tian, Yudong, Grey S. Nearing, Christa D. Peters-Lidard, Kenneth W. Harrison, and Ling Tang. "Performance Metrics, Error Modeling, and Uncertainty Quantification." Monthly Weather Review 144, no. 2 (February 1, 2016): 607–13. http://dx.doi.org/10.1175/mwr-d-15-0087.1.

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Abstract A common set of statistical metrics has been used to summarize the performance of models or measurements—the most widely used ones being bias, mean square error, and linear correlation coefficient. They assume linear, additive, Gaussian errors, and they are interdependent, incomplete, and incapable of directly quantifying uncertainty. The authors demonstrate that these metrics can be directly derived from the parameters of the simple linear error model. Since a correct error model captures the full error information, it is argued that the specification of a parametric error model should be an alternative to the metrics-based approach. The error-modeling methodology is applicable to both linear and nonlinear errors, while the metrics are only meaningful for linear errors. In addition, the error model expresses the error structure more naturally, and directly quantifies uncertainty. This argument is further explained by highlighting the intrinsic connections between the performance metrics, the error model, and the joint distribution between the data and the reference.

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11

Liaw, C. M., K. S. Jaw, and Y. S. Gong. "Linear Model Following Control Systems Based on Reduced Models." Journal of Dynamic Systems, Measurement, and Control 114, no. 2 (June 1, 1992): 324–27. http://dx.doi.org/10.1115/1.2896533.

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A linear model following controller based on a reduced-order plant model is presented in this paper. The reduced model is composed of two submodels that characterize the dynamics during the transient and the steady states. The order of the controller is minimized without deteriorating the system performance. In addition, the proposed modified linear model following controller, which uses the output as the feedback signal, can be easily implemented. Some simulation and experimental results are given to demonstrate the effectiveness of the proposed controller.

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12

de Jesus, Karla, Helon V. H. Ayala, Kelly de Jesus, Leandro dos S. Coelho, Alexandre I. A. Medeiros, José A. Abraldes, Mário A. P. Vaz, Ricardo J. Fernandes, and João Paulo Vilas-Boas. "Modelling and Predicting Backstroke Start Performance Using Non-Linear And Linear Models." Journal of Human Kinetics 61, no. 1 (March 23, 2018): 29–38. http://dx.doi.org/10.1515/hukin-2017-0133.

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AbstractOur aim was to compare non-linear and linear mathematical model responses for backstroke start performance prediction. Ten swimmers randomly completed eight 15 m backstroke starts with feet over the wedge, four with hands on the highest horizontal and four on the vertical handgrip. Swimmers were videotaped using a dual media camera set-up, with the starts being performed over an instrumented block with four force plates. Artificial neural networks were applied to predict 5 m start time using kinematic and kinetic variables and to determine the accuracy of the mean absolute percentage error. Artificial neural networks predicted start time more robustly than the linear model with respect to changing training to the validation dataset for the vertical handgrip (3.95 ± 1.67 vs. 5.92 ± 3.27%). Artificial neural networks obtained a smaller mean absolute percentage error than the linear model in the horizontal (0.43 ± 0.19 vs. 0.98 ± 0.19%) and vertical handgrip (0.45 ± 0.19 vs. 1.38 ± 0.30%) using all input data. The best artificial neural network validation revealed a smaller mean absolute error than the linear model for the horizontal (0.007 vs. 0.04 s) and vertical handgrip (0.01 vs. 0.03 s). Artificial neural networks should be used for backstroke 5 m start time prediction due to the quite small differences among the elite level performances.

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13

Babaei, Mohammadreza, Lütfiye Durak-Ata, and Ümit Aygölü. "Performance Analysis of Dual-Hop AF Relaying with Non-Linear/Linear Energy Harvesting." Sensors 22, no. 16 (August 10, 2022): 5987. http://dx.doi.org/10.3390/s22165987.

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Massive device-to-device communication nodes and Internet of Things (IoT) devices are expected to be crucial components in next-generation wireless networks. However, the energy constraint of these nodes presents a challenge since the energy of the batteries is limited. Motivated by this, radio frequency energy harvesting (EH) has been developed as an efficient strategy to overcome the energy constraint of IoT devices and sensor networks. In this paper, a wireless-powered dual-hop amplify-and-forward relaying system, in the absence of a direct link between the source (S) and the destination (D), is considered. It is assumed that a dedicated power beacon (PB) transmits an energy-bearing signal from which the power-constrained S and relay (R) harvest energy. Theoretical derivations of bit error probability, outage probability, and throughput expressions are performed for both linear and non-linear energy harvesting models. Moreover, the theoretical results provided for different system parameters are validated via Monte Carlo simulations. The obtained results reveal the difference between the realistic non-linear EH model and the conventional linear EH model, which overestimates the system performance at high levels of harvested energy. Thus, it leads to misunderstanding the real performance of the EH systems. However, at low levels of harvested energy, both models behave similarly and provide realistic results.

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14

Javadi Moghaddam, Jalal, Ghasem Zarei, Davood Momeni, and Hamideh Faridi. "Non-linear control model for use in greenhouse climate control systems." Research in Agricultural Engineering 68, No. 1 (March 23, 2022): 9–17. http://dx.doi.org/10.17221/37/2021-rae.

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In this study, a non-linear control system was designed and proposed to control the greenhouse climate conditions. This control system directly uses the information of sensors, installed inside and outside the greenhouse. To design this proposed control system, the principles of a non-linear control system and the concepts of equilibrium points and zero dynamics of system theories were used. To show the capability and applicability of the proposed control system, it was compared with an integral sliding mode controller. A greenhouse with similar climatic conditions was used to simulate the performance of the integral sliding mode controller. In this study, it was seen that the integral sliding mode control system was more accurate; however, the actuator signals sent by this control system were not smooth. It could damage and depreciate the greenhouse equipment more quickly than the proposed non-linear control system. It was also shown that the regulation of the temperature and humidity was performed very smoothly by changing the reference signals according to the weather conditions outside the greenhouse. The ability of these two control systems was graphically demonstrated for temperature and humidity responses as well as for the signals sent to the actuators.

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15

Manguoglu, Murat, Faisal Saied, Ahmed Sameh, and Ananth Grama. "Performance Models for the Spike Banded Linear System Solver." Scientific Programming 19, no. 1 (2011): 13–25. http://dx.doi.org/10.1155/2011/426421.

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With availability of large-scale parallel platforms comprised of tens-of-thousands of processors and beyond, there is significant impetus for the development of scalable parallel sparse linear system solvers and preconditioners. An integral part of this design process is the development of performance models capable of predicting performance and providing accurate cost models for the solvers and preconditioners. There has been some work in the past on characterizing performance of the iterative solvers themselves. In this paper, we investigate the problem of characterizing performance and scalability of banded preconditioners. Recent work has demonstrated the superior convergence properties and robustness of banded preconditioners, compared to state-of-the-art ILU family of preconditioners as well as algebraic multigrid preconditioners. Furthermore, when used in conjunction with efficient banded solvers, banded preconditioners are capable of significantly faster time-to-solution. Our banded solver, the Truncated Spike algorithm is specifically designed for parallel performance and tolerance to deep memory hierarchies. Its regular structure is also highly amenable to accurate performance characterization. Using these characteristics, we derive the following results in this paper: (i) we develop parallel formulations of the Truncated Spike solver, (ii) we develop a highly accurate pseudo-analytical parallel performance model for our solver, (iii) we show excellent predication capabilities of our model – based on which we argue the high scalability of our solver. Our pseudo-analytical performance model is based on analytical performance characterization of each phase of our solver. These analytical models are then parameterized using actual runtime information on target platforms. An important consequence of our performance models is that they reveal underlying performance bottlenecks in both serial and parallel formulations. All of our results are validated on diverse heterogeneous multiclusters – platforms for which performance prediction is particularly challenging. Finally, we provide predict the scalability of the Spike algorithm using up to 65,536 cores with our model. In this paper we extend the results presented in the Ninth International Symposium on Parallel and Distributed Computing.

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Yu, Xingji, Laurent Georges, and Lars Imsland. "Adaptive Linear Grey-Box Models for Model Predictive Controller of Residential Buildings." E3S Web of Conferences 362 (2022): 12001. http://dx.doi.org/10.1051/e3sconf/202236212001.

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Model predictive control (MPC) is an advanced optimal control technique to minimize a control objective while satisfying a set of constraints and is well suited to activate the building energy flexibility. The MPC controller performance depends on the accuracy of the model prediction. Inaccurate predictions can directly lead to low control performance. Linear time-invariant (LTI) models are often used in MPC in buildings. However, LTI models do not adapt to the weather conditions varying throughout the whole space-heating season, which makes the MPC based on LTI models not perform well over a long period of time. Therefore, this study introduces an adaptive MPC where the parameters of a linear grey-box model are continuously updated in real-time. Two alternative versions of this adaptive control are investigated. The first one, called partially adaptive MPC, only updates the effective window area of the grey-box model, while the second one, called fully adaptive MPC, updates all the parameters of the grey-box model. Results show that the partially adaptive MPC is not able to deliver satisfactory prediction performance. The fully adaptive MPC shows better performance compared to the other models when implemented in a MPC, especially in avoiding thermal comfort violation.

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17

Gyurik, Casper, Dyon Vreumingen, van, and Vedran Dunjko. "Structural risk minimization for quantum linear classifiers." Quantum 7 (January 13, 2023): 893. http://dx.doi.org/10.22331/q-2023-01-13-893.

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Quantum machine learning (QML) models based on parameterized quantum circuits are often highlighted as candidates for quantum computing's near-term “killer application''. However, the understanding of the empirical and generalization performance of these models is still in its infancy. In this paper we study how to balance between training accuracy and generalization performance (also called structural risk minimization) for two prominent QML models introduced by Havlíček et al. \cite{havlivcek:qsvm}, and Schuld and Killoran \cite{schuld:qsvm}. Firstly, using relationships to well understood classical models, we prove that two model parameters – i.e., the dimension of the sum of the images and the Frobenius norm of the observables used by the model – closely control the models' complexity and therefore its generalization performance. Secondly, using ideas inspired by process tomography, we prove that these model parameters also closely control the models' ability to capture correlations in sets of training examples. In summary, our results give rise to new options for structural risk minimization for QML models.

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18

Naylor, D., and S. Jones. "A performance model for multilayer neural networks in linear arrays." IEEE Transactions on Parallel and Distributed Systems 5, no. 12 (1994): 1322–28. http://dx.doi.org/10.1109/71.334906.

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19

Walde, Getinet Seifu. "Hierarchical linear model to examine determinants of students’ mathematics performance." Journal of Physics: Conference Series 1176 (March 2019): 042088. http://dx.doi.org/10.1088/1742-6596/1176/4/042088.

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20

Helmers, Henning, and Korbinian Kramer. "Multi-linear performance model for hybrid (C)PVT solar collectors." Solar Energy 92 (June 2013): 313–22. http://dx.doi.org/10.1016/j.solener.2013.03.003.

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21

Arashi, M., and T. Valizadeh. "Performance of Kibria’s methods in partial linear ridge regression model." Statistical Papers 56, no. 1 (January 17, 2014): 231–46. http://dx.doi.org/10.1007/s00362-014-0578-6.

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22

Hassid, S. "A linear model for passive solar calculations: Evaluation of performance." Building and Environment 20, no. 1 (January 1985): 53–59. http://dx.doi.org/10.1016/0360-1323(85)90032-0.

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23

Zhou, Yanshuang, Na Li, Hong Li, and Yongqiang Zhang. "Regression Cloud Models and Their Applications in Energy Consumption of Data Center." Journal of Electrical and Computer Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/143071.

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As cloud data center consumes more and more energy, both researchers and engineers aim to minimize energy consumption while keeping its services available. A good energy model can reflect the relationships between running tasks and the energy consumed by hardware and can be further used to schedule tasks for saving energy. In this paper, we analyzed linear and nonlinear regression energy model based on performance counters and system utilization and proposed a support vector regression energy model. For performance counters, we gave a general linear regression framework and compared three linear regression models. For system utilization, we compared our support vector regression model with linear regression and three nonlinear regression models. The experiments show that linear regression model is good enough to model performance counters, nonlinear regression is better than linear regression model for modeling system utilization, and support vector regression model is better than polynomial and exponential regression models.

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24

El-Saeiti, Intesar N., and Khalil Mostafa ALsawi. "PERFORMANCE OF MIXED EFFECTS FOR CLUSTERED COUNTING DATA MODELS." Scientific Journal of Applied Sciences of Sabratha University 3, no. 2 (September 27, 2020): 34–41. http://dx.doi.org/10.47891/sabujas.v3i2.34-41.

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This article is concerned with hierarchical generalized linear models. It includes generalized linear models and generalized linear mixed models, which are related to linear models. In generalized linear mixed models, the dependent variable and the standard error follow any distribution from the exponential family, e.g. normal, Poisson, binomial, gamma, etc. We studied counting data, and then use the Poisson-gamma model,where the dependentvariable follows the Poisson distribution and the standard error follow the gamma distribution. Several estimation techniques can be used for generalized linear mixed model. In this paperthe hierarchical likelihood estimation technique was used to prove the performance of H-likelihood methodwhen thecounting data were balanced or unbalanced. Real data were used to test the performance of Poisson-gamma H-likelihood estimation method in case of balanced and unbalanced counting data.When real data used in the past research for another problem, it was noticed that the performance of the hierarchical likelihood estimation technique gave a close approximations in the event of balanced and unbalanced counting data, and the output of the technique was approximately equivalent in both instances.

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25

Vijayalakshmi, S., and D. Manamalli. "LPV Modeling and Performance Analysis of Boiler Drum Using IMC-PI Controller." Applied Mechanics and Materials 415 (September 2013): 117–21. http://dx.doi.org/10.4028/www.scientific.net/amm.415.117.

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This paper presents the development of linear parameter varying model and control strategy for Industrial boiler drum. The Boiler drum is modeled using the fundamental mass and energy balance equations and data collected from the real time plant. Based on the open loop response of the Boiler drum, the whole non-linear process is split into 4 approximate linear regions and their respective transfer function models are formed from the input-output data. Since the process is nonlinear in nature, linear transfer function models are developed in each operating regions and they are interpolated to obtain the linear parameter varying model. The LPV model is validated using the real time data. Since a single PI controller for whole process does not satisfy both servo and regulatory performance, a multi-model approach with four PI controllers is implemented for the process. Internal Model Control ( IMC)-PI controller is designed to analysis the servo and regulatory performance of the developed LPV model.

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26

Lee, Kwang-Ho, and Yong-Hwan Cho. "Simple Breaker Index Formula Using Linear Model." Journal of Marine Science and Engineering 9, no. 7 (July 1, 2021): 731. http://dx.doi.org/10.3390/jmse9070731.

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Breaking waves generated by wave shoaling in coastal areas have a close relationship with various physical phenomena in coastal regions. Therefore, it is crucial to accurately predict breaker indexes such as breaking wave height and breaking depth when designing coastal structures. Many studies on wave breaking have been carried out, and many experimental data have been documented. Representative studies on wave breaking provide many empirical formulas for the prediction of breaking index, mainly through hydraulic model experiments. However, the existing empirical formulas for breaking index determine the coefficients of the assumed equation through statistical analysis of data under the assumption of a specific equation. This study presents an alternative method to estimate breaker index using representative linear-based supervised machine learning algorithms that show high predictive performance in various research fields related to regression or classification problems. Based on the used machine learning methods, a new simple linear equation for the prediction of breaker index is presented. The newly proposed breaker index formula showed similar predictive performance compared to the existing empirical formula, although it was a simple linear equation.

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27

Bai, Chao, and Haiqi Li. "Simultaneous prediction in the generalized linear model." Open Mathematics 16, no. 1 (August 24, 2018): 1037–47. http://dx.doi.org/10.1515/math-2018-0087.

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AbstractThis paper studies the prediction based on a composite target function that allows to simultaneously predict the actual and the mean values of the unobserved regressand in the generalized linear model. The best linear unbiased prediction (BLUP) of the target function is derived. Studies show that our BLUP has better properties than some other predictions. Simulations confirm its better finite sample performance.

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28

Birnbaum, Dee, and Mark John Somers. "Fitting Job Performance into Turnover Model: An Examination of the Form of the Job Performance-Turnover Relationship and a Path Model." Journal of Management 19, no. 1 (February 1993): 1–11. http://dx.doi.org/10.1177/014920639301900101.

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Linear and curvilinear components of job performance were studied in relation to turnover. Neither the linear nor the curvilinear hypothesis was supported as job performance was unrelated to turnover. Other higher-order effects were also not evident as job satisfaction did not moderate the job performance-turnover relationship. Using a path model, indirect linkages between job performance and job satisfaction and job performance and job search were investigated. Niether linkage was supported suggesting that job performance is not central to the turnover process. Implications of these findings for future research were discussed.

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29

Kwag, Shinyoung, Daegi Hahm, Minkyu Kim, and Seunghyun Eem. "Development of a Probabilistic Seismic Performance Assessment Model of Slope Using Machine Learning Methods." Sustainability 12, no. 8 (April 17, 2020): 3269. http://dx.doi.org/10.3390/su12083269.

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The objective of this study is to propose a model that can predict the seismic performance of slope relatively accurately and efficiently by using machine learning methods. Probabilistic seismic fragility analyses of the slope had been carried out in other studies, and a closed-form equation for slope seismic performance was proposed through a multiple linear regression analysis. However, the traditional statistical linear regression analysis showed a limit that could not accurately represent such nonlinear slope seismic performances. To overcome this limit, in this study, we used three machine learning methods (i.e., support vector machine (SVM), artificial neural network (ANN), Gaussian process regression (GPR)) to generate prediction models of the slope seismic performance. The models obtained through the machine learning methods basically showed better performance compared to the models of the traditional statistical methods. The results of the SVM showed no significant performance difference compared with the results of the nonlinear regression analysis method, but the results based on the ANN and GPR showed a remarkable improvement in the prediction performance over the other models. Furthermore, this study confirmed that the GPR-based model predicted relatively accurate seismic performance values compared with the model through the ANN.

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30

Wang, Wei, and Yong Ming Xia. "Research on Linear Motor for Linear Compressor." Advanced Materials Research 383-390 (November 2011): 1350–55. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.1350.

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A novel moving-magnet linear motor was introduced which can produce high-frequency short stroke reciprocating motion, and it’s suitable to apply in linear compressor. The equivalent magnetic circuit model was established and the expression of driven force was given, then the impact the motor parameters made to motor performance was analyzed. The motor’s finite element model was established, the features of the air-gap magnetic field were studied, and the impact the length of the permanent magnet made to the motor stroke was analyzed. The foundation of the motor’s further optimization was laid by the results.

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Wang, Wei, and Yong Ming Xia. "Research on Linear Motor for Linear Compressor." Advanced Materials Research 433-440 (January 2012): 2635–40. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.2635.

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A novel moving-magnet linear motor was introduced which can produce high-frequency short stroke reciprocating motion, and it’s suitable to apply in linear compressor. The equivalent magnetic circuit model was established and the expression of driven force was given, then the impact the motor parameters made to motor performance was analyzed. The motor’s finite element model was established, the features of the air-gap magnetic field were studied, and the impact the length of the permanent magnet made to the motor stroke was analyzed. The foundation of the motor’s further optimization was laid by the results.

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32

Jiang, Xing Yu, Chao Gao, Wei Xian Gao, Peng Cheng Su, and Hai Feng Zhao. "A New Integrated Performance Forecasting Model of TBM." Key Engineering Materials 693 (May 2016): 439–44. http://dx.doi.org/10.4028/www.scientific.net/kem.693.439.

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To realize accurate prediction of disk cutter performance and determine the crucial parameters in the design process of TBM (Full face tunneling boring machine), a new performance forecasting method of the circular blade cutter is presented, which integrates the parameters of breaking rock such as rock, disc cutter and construction. Furthermore, the second derivation in the process of breaking rock is considered, a new comprehensive performance forecasting model of the circular blade cutter is established based on the Coulomb-Mohr failure criterion. On the basis, the existed performance forecasting models of disc cutter such as Liner Cutting and CSM, and the performance forecasting model of disc cutter presented this paper are analyzed and calculated, according to Colorado linear cutting experiment. The calculation results indicate the prediction accuracy of the performance forecasting model presented in this paper is greatly improved, comparing with the existed models (such as CSM, linear cutting). Finally, the influences of the parameters (rock, disc cutter and construction) are analyzed, which provides for the overall design of TBM cutter and construction with some scientific basis.

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33

Hawari, Alaa H., and Wael Alnahhal. "Predicting the performance of multi-media filters using artificial neural networks." Water Science and Technology 74, no. 9 (August 27, 2016): 2225–33. http://dx.doi.org/10.2166/wst.2016.380.

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The impact of flow rate and turbidity on the performance of multi-media filtration has been studied using an artificial neural network (ANN) based model. The ANN model was developed and tested based on experimental data collected from a pilot scale multi-media filter system. Several ANN models were tested, and the best results with the lowest errors were achieved with two hidden layers and five neurons per layer. To examine the significance and efficiency of the developed ANN model it was compared with a linear regression model. The R2 values for the actual versus predicted results were 0.9736 and 0.9617 for the ANN model and the linear regression model, respectively. The ANN model showed an R-squared value increase of 1.22% when compared to the linear regression model. In addition, the ANN model gave a significant reduction of 91.5% and 97.9% in the mean absolute error and the root mean square error, respectively when compared to the linear regression model. The proposed model has proven to give plausible results to model complex relationships that can be used in real life water treatment plants.

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Abdul Kareem, Ali Fawzi, and Ahmed Abdul Hussein Ali. "Robust Stability Control of Inverted Pendulum Model for Bipedal Walking Robot." Al-Nahrain Journal for Engineering Sciences 23, no. 1 (March 20, 2020): 81–88. http://dx.doi.org/10.29194/njes.23010081.

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This paper proposes robust control for three models of the linear inverted pendulum (one mass linear inverted pendulum model, two masses linear inverted pendulum model and three masses linear inverted pendulum model) which represents the upper, middle and lower body of a bipedal walking robot. The bipedal walking robot is built of light-weight and hard Aluminum sheets with 2 mm thickness. The minimum phase system and non-minimum phase system are studied and investigated for inverted pendulum models. The bipedal walking robot is programmed by Arduino microcontroller UNO. A MATLAB Simulink system is built to embrace the theoretical work. The results showed that one linear inverted pendulum is the worst performance, worst noise rejection and the worst set point tracking to the zero moment point. But two masses linear inverted pendulum models and three masses linear inverted pendulum model have a better performance, a better high-frequency noise rejection characteristic and better set-point tracking to the zero moment point.

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35

Wang, Dashun, Di Niu, and Huazhou Andy Li. "Predicting Waterflooding Performance in Low-Permeability Reservoirs With Linear Dynamical Systems." SPE Journal 22, no. 05 (May 16, 2017): 1596–608. http://dx.doi.org/10.2118/185960-pa.

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Summary Several interwell connectivity models such as multiple linear regression (MLR) and the capacitance model (CM) have been proposed to model waterflooding performance in high-permeability reservoirs on the basis of observed production data. However, the existing methods are not effective at characterizing the behavior of transient flows that are prevalent in low-permeability reservoirs. This paper presents a novel dynamic waterflooding model that is based on linear dynamical systems (LDSs) to characterize the injection/production relationships in an oil field during both stationary and nonstationary production phases. We leverage a state-space model (SSM), in which the changing rates of control volumes between injector/producer pairs in the reservoir of interest serve as time-varying hidden states, depending on the reservoir condition. Thus, the model can better characterize the transient dynamics in low-permeability reservoirs. We propose a self-learning procedure for the model to train its parameters as well as the evolution of the hidden states only on the basis of past observations of injection and production rates. We tested the LDS method in comparison with the state-of-the-art CM method in a wide range of synthetic reservoir models including both high-permeability and low-permeability reservoirs, as well as various dynamic scenarios involving varying bottomhole pressure (BHP) of producers, injector shut-ins, and reservoirs of larger scales. We also tested LDS on the real production data collected from Changqing oil field containing low-permeability formations. Testing results demonstrate that an LDS significantly outperforms CM in terms of modeling and predicting waterflooding performance in low-permeability reservoirs and various dynamic scenarios.

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Al-Qaisy, Muayad A. Shehab. "Linear and Non-linear Multi-Input Multi-Output Model Predictive Control of Continuous Stirred Tank Reactor." Tikrit Journal of Engineering Sciences 19, no. 3 (September 30, 2012): 41–57. http://dx.doi.org/10.25130/tjes.19.3.05.

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In this article, multi-input multi-output (MIMO) linear model predictive controller (LMPC) based on state space model and nonlinear model predictive controller based on neural network (NNMPC) are applied on a continuous stirred tank reactor (CSTR). The idea is to have a good control system that will be able to give optimal performance, reject high load disturbance, and track set point change. In order to study the performance of the two model predictive controllers, MIMO Proportional-Integral-Derivative controller (PID) strategy is used as benchmark. The LMPC, NNMPC, and PID strategies are used for controlling the residual concentration (CA) and reactor temperature (T). NNMPC control shows a superior performance over the LMPC and PID controllers by presenting a smaller overshoot and shorter settling time.

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Kadi, L., B. Bourges, and J. Adnot. "The Input-Output Model for Solar Water Heaters: Model Errors and Long-Term Performance Estimate." Journal of Solar Energy Engineering 112, no. 3 (August 1, 1990): 161–68. http://dx.doi.org/10.1115/1.2930475.

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Based on the experimentally observed linear relationship between the daily collected energy and the daily insolation, the development of a test method for the performance rating of solar water heaters is studied using both an analytical modeling of the daily performance and detailed simulation. This analysis has shown the existence of a general linear correlation between daily performance and external conditions (daily irradiation, ambient temperature, main’s water temperature, and storage initial temperature). Two approaches appeared possible for the long-term performance estimate and, for its simplicity, the statistical one was chosen for the definition of the European test method for solar water heaters, also known as the input-output test method.

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Tao, Tianyou, Peng Shi, Hao Wang, Lin Yuan, and Sheng Wang. "Performance Evaluation of Linear and Nonlinear Models for Short-Term Forecasting of Tropical-Storm Winds." Applied Sciences 11, no. 20 (October 11, 2021): 9441. http://dx.doi.org/10.3390/app11209441.

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Wind-sensitive structures usually suffer from violent vibrations or severe damages under the action of tropical storms. It is of great significance to forecast tropical-storm winds in advance for the sake of reducing or avoiding consequent losses. The model used for forecasting becomes a primary concern in engineering applications. This paper presents a performance evaluation of linear and nonlinear models for the short-term forecasting of tropical storms. Five extensively employed models are adopted to forecast wind speeds using measured samples from the tropical storm Rumbia, which facilitates a comparison of the predicting performances of different models. The analytical results indicate that the autoregressive integrated moving average (ARIMA) model outperforms the other models in the one-step ahead prediction and presents the least forecasting errors in both the mean and maximum wind speeds. However, the support vector regression (SVR) model has the worst performance on the selected dataset. When it comes to the multi-step ahead forecasting, the prediction error of each model increases as the number of steps expands. Although each model shows an insufficient ability to capture the variation of future wind speed, the ARIMA model still appears to have the least forecasting errors. Hence, the ARIMA model can offer effective short-term forecasting of tropical-storm winds in both one-step and multi-step scenarios.

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SIMSEK, ZEKI. "CEO TENURE AND ORGANIZATIONAL PERFORMANCE: TESTING A NON-LINEAR INTERVENING MODEL." Academy of Management Proceedings 2004, no. 1 (August 2004): O1—O6. http://dx.doi.org/10.5465/ambpp.2004.13863781.

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Wang, Yu Ning, Hui Ming Zeng, and Bin Xiang Hu. "Economic Performance Model of New Energy Vehicles Based on Linear Programming." Applied Mechanics and Materials 389 (August 2013): 97–101. http://dx.doi.org/10.4028/www.scientific.net/amm.389.97.

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In order to evaluate the economy performance of new energy vehicles, the life-cycle cost theory is applied. A formula of quantitative analysis in combination is adopted to have in-depth study on the life-cycle cost of new energy vehicles. Analysis and argumentation are also conducted in comparison pure-electric vehicles, hybrid electric vehicles and traditional oil-fueled vehicles under the condition of present oil price, the analysis result shows that if despite the battery cost, PEV costs the fewest because of the lowest operation costs and energy costs, the cost of HEV is at a middle level. Therefore, customers and manufacturers may become more interested in PEV than in other solutions. Furthermore, a decision tool by setting up a cost model based on linear programming theory is developed, economic influence factors for the application of new energy vehicles are further discussed through sensitivity analysis.

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Wu, Jibo, and Chaolin Liu. "Performance of Some Stochastic Restricted Ridge Estimator in Linear Regression Model." Journal of Applied Mathematics 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/508793.

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This paper considers several estimators for estimating the stochastic restricted ridge regression estimators. A simulation study has been conducted to compare the performance of the estimators. The result from the simulation study shows that stochastic restricted ridge regression estimators outperform mixed estimator. A numerical example has been also given to illustrate the performance of the estimators.

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Lalitha, S., and P. C. Joshi. "PERFORMANCE OF MURPHY'S TEST FOR TWO OUTLIERS IN A LINEAR MODEL." Statistica Neerlandica 40, no. 2 (June 1986): 99–107. http://dx.doi.org/10.1111/j.1467-9574.1986.tb01195.x.

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Solar, H., G. Bistué, J. Legarda, E. Fernández, and R. Berenguer. "Design model for fully integrated high-performance linear CMOS power amplifiers." IET Microwaves, Antennas & Propagation 5, no. 7 (2011): 795. http://dx.doi.org/10.1049/iet-map.2010.0217.

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Frison, Gianluca, Leo Emil Sokoler, and John Bagterp Jørgensen. "A Family of High-Performance Solvers for Linear Model Predictive Control." IFAC Proceedings Volumes 47, no. 3 (2014): 3074–79. http://dx.doi.org/10.3182/20140824-6-za-1003.01302.

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Francis, Royce A., Srinivas Reddy Geedipally, Seth D. Guikema, Soma Sekhar Dhavala, Dominique Lord, and Sarah LaRocca. "Characterizing the Performance of the Conway-Maxwell Poisson Generalized Linear Model." Risk Analysis 32, no. 1 (July 30, 2011): 167–83. http://dx.doi.org/10.1111/j.1539-6924.2011.01659.x.

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Qasim, Muhammad, Muhammad Amin, and Talha Omer. "Performance of some new Liu parameters for the linear regression model." Communications in Statistics - Theory and Methods 49, no. 17 (April 19, 2019): 4178–96. http://dx.doi.org/10.1080/03610926.2019.1595654.

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Seron, Maria M., Graham C. Goodwin, and Diego S. Carrasco. "Stochastic model predictive control: Insights and performance comparisons for linear systems." International Journal of Robust and Nonlinear Control 29, no. 15 (April 23, 2018): 5038–57. http://dx.doi.org/10.1002/rnc.4106.

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Quang, Nguyen Hong, Nguyen Phung Quang, Dao Phuong Nam, and Nguyen Thanh Binh. "Multi parametric model predictive control based on laguerre model for permanent magnet linear synchronous motors." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (April 1, 2019): 1067. http://dx.doi.org/10.11591/ijece.v9i2.pp1067-1077.

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<p>The permanent magnet linear motors are widely used in various industrial applications due to its advantages in comparisons with rotary motors such as mechanical durability and directly creating linear motions without gears or belts. The main difficulties of its control design are that the control performances include the tracking of position and velocity as well as guarantee limitations of the voltage control and its variation. In this work, a cascade control strategy including an inner and an outer loop is applied to synchronous linear motor. Particularly, an offline MPC controller based on MPP method and Laguerre model was proposed for inner loop and the outer controller was designed with the aid of nonlinear damping method. The numerical simulation was implemented to validate performance of the proposed controller under voltage input constraints.</p>

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Tozaki, T., T. Miyake, H. Kakoi, H. Gawahara, K. Hirota, Y. Nakano, and M. Kurosawa. "Heritability estimates for racing performance in Japanese Thoroughbred racehorses using linear and non-linear model analyses." Journal of Animal Breeding and Genetics 129, no. 5 (December 28, 2011): 402–8. http://dx.doi.org/10.1111/j.1439-0388.2011.00982.x.

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Tso, S. K., M. L. Lai, and P. L. Law. "Variable-Structure Linear-Model-Following Control of Manipulators." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 207, no. 1 (February 1993): 35–45. http://dx.doi.org/10.1243/pime_proc_1993_207_313_02.

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The paper describes the interaction between modelling, control and adaptation of a variable-structure model-following method applied to highly non-linear plants. The method is particularly appealing in the control of high-performance robot manipulators. The problems concerned with its application are discussed with reference to the use of a controller in a commercial manipulator as a case study.

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