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Identification of Nonlinear Systems Using a New Neuro-Fuzzy Dynamical System Definition Based on High Order Neural Network Function Approximators

Identification of Nonlinear Systems Using a New Neuro-Fuzzy Dynamical System Definition Based on High Order Neural Network Function Approximators

Yiannis S. Boutalis, M. A. Christodoulou, Dimitris C. Theodoridis
Copyright: © 2010 |Pages: 27
ISBN13: 9781615207114|ISBN10: 1615207112|EISBN13: 9781615207121
DOI: 10.4018/978-1-61520-711-4.ch018
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MLA

Boutalis, Yiannis S., et al. "Identification of Nonlinear Systems Using a New Neuro-Fuzzy Dynamical System Definition Based on High Order Neural Network Function Approximators." Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications, edited by Ming Zhang, IGI Global, 2010, pp. 423-449. https://doi.org/10.4018/978-1-61520-711-4.ch018

APA

Boutalis, Y. S., Christodoulou, M. A., & Theodoridis, D. C. (2010). Identification of Nonlinear Systems Using a New Neuro-Fuzzy Dynamical System Definition Based on High Order Neural Network Function Approximators. In M. Zhang (Ed.), Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications (pp. 423-449). IGI Global. https://doi.org/10.4018/978-1-61520-711-4.ch018

Chicago

Boutalis, Yiannis S., M. A. Christodoulou, and Dimitris C. Theodoridis. "Identification of Nonlinear Systems Using a New Neuro-Fuzzy Dynamical System Definition Based on High Order Neural Network Function Approximators." In Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications, edited by Ming Zhang, 423-449. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-61520-711-4.ch018

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Abstract

A new definition of adaptive dynamic fuzzy systems (ADFS) is presented in this chapter for the identification of unknown nonlinear dynamical systems. The proposed scheme uses the concept of adaptive fuzzy systems operating in conjunction with high order neural networks (HONN’s). Since the plant is considered unknown, we first propose its approximation by a special form of an adaptive fuzzy system and in the sequel the fuzzy rules are approximated by appropriate HONN’s. Thus the identification scheme leads up to a recurrent high order neural network, which however takes into account the fuzzy output partitions of the initial ADFS. Weight updating laws for the involved HONN’s are provided, which guarantee that the identification error reaches zero exponentially fast. Simulations illustrate the potency of the method and comparisons on well known benchmarks are given.

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