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A Method to Model Nonlinear Systems by Neural Networks
Xifan Yao1; Dongyuan Ge1; Zhaotong Lian2
2009
Conference Name2009 Fourth International Conference on Innovative Computing, Information and Control
Source Publication2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC)
Conference Date2009-12.7-9
Conference PlaceKaohsiung, Taiwan
Abstract

Many processes in reality exhibit nonlinear characteristics and in most of cases they cannot be treated satisfactorily using linearized approach in a large operating range. In this paper, an approximate approach is introduced to overcome the inaccuracy and inconsistency between the linearized model and the real process, due to linear representation of the nonlinear system, such as using Taylor series expansion by treating the nonlinear system as a linear uncertain system, that consists of a linear part, and an uncertain part. A neural network with Gaussian radial basis function in the hidden layer is employed to approximate the uncertain system. The approach can incorporate prior knowledge in its framework and provide a more transparent insight than the neural “black box” approach. The simulation results reveal that the proposed modeling approach to nonlinear systems is effective.

KeywordNonlinear System Modeling Neural Network Approximation Linearization
DOI10.1109/ICICIC.2009.27
Language英语
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Document TypeConference paper
CollectionDEPARTMENT OF ACCOUNTING AND INFORMATION MANAGEMENT
Faculty of Business Administration
Affiliation1.School of Mechanical & Automotive Engineering, South China University of Technology
2.Faculty of Business Administration, University of Macau
Recommended Citation
GB/T 7714
Xifan Yao,Dongyuan Ge,Zhaotong Lian. A Method to Model Nonlinear Systems by Neural Networks[C],2009.
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