The prediction accuracy and generalization ability of neural/neurofuzzy models for chaotic time series prediction highly depends on employed network model as well as learning algorithm. In this study, several neural a...
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The prediction accuracy and generalization ability of neural/neurofuzzy models for chaotic time series prediction highly depends on employed network model as well as learning algorithm. In this study, several neural and neurofuzzy models with different learning algorithms are examined for prediction of several benchmark chaotic systems and time series. The prediction performance of locallylinear neurofuzzy models with recently developed locally linear model tree (LoLiMoT) learning algorithm is compared with that of Radial Basis Function (RBF) neural network with Orthogonal Least Squares (OLS) learning algorithm, MultiLayer Perceptron neural network with error back-propagation learning algorithm, and Adaptive Network based Fuzzy Inference System. Particularly, cross validation techniques based on the evaluation of error indices on multiple validation sets is utilized to optimize the number of neurons and to prevent over fitting in the incremental learning algorithms. To make a fair comparison between neural and neurofuzzy models, they are compared at their best structure based on their prediction accuracy, generalization, and computational complexity. The experiments are basically designed to analyze the generalization capability and accuracy of the learning techniques when dealing with limited number of training samples from deterministic chaotic time series, but the effect of noise on the performance of the techniques is also considered. Various chaotic systems and time series including Lorenz system, Mackey-Glass chaotic equation, Henon map, AE geomagnetic activity index, and sunspot numbers are examined as case studies. The obtained results indicate the superior performance of incremental learning algorithms and their respective networks, such as, OLS for RBF network and LoLiMoT for locallylinear neurofuzzy model.
This paper, presents an implementation of a predictive control algorithm using locally linear model tree (LOLIMOT) as the system identification algorithm, on an industry case study: solution copolymerization of methyl...
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ISBN:
(纸本)0780382781
This paper, presents an implementation of a predictive control algorithm using locally linear model tree (LOLIMOT) as the system identification algorithm, on an industry case study: solution copolymerization of methylmethacrylate and vinyl acetate. The nonlinear system is represented with a model based on LOLIMOT algorithm. The control approach is successfully implemented during a transient from one operating point to another. Significant improvement is observed in reducing the transient period. Simulation results conform the effectiveness of the proposed predictive control strategy.
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