Identification of linear and nonlinear dynamic systems using recurrent neural networks |
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| Affiliation: | 1. Shanghai Institute of Applied Mathematics and Mechanics, Shanghai University, Shanghai 200072, China;2. School of Urban Railway Transportation, Shanghai University of Engineering Science, Shanghai 201620, China;3. Department of Mechanics, Shanghai University, Shanghai 200444, China;4. Shanghai Key Laboratory of Mechanics in Energy Engineering, Shanghai University, Shanghai 200072, China;1. Department of Mechanical Engineering, National Institute of Technology Silchar, Silchar, India;2. Department of Mechanical Engineering, Birla Institute of Technology Mesra, Ranchi, India;1. Sri Venkateswara Engineering College, Tirupati, India;2. Aditya Engineering College (A), Surampalem, A.P, India |
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| Abstract: | This paper describes the use of Elman-type recurrent neural networks to identify dynamic systems. Networks as originally designed by Elman (Cognitive Sci., 1990, 14, 179–211) and also those in which self-connections are made to the context units were employed to identify a variety of linear and nonlinear systems. It was found that the latter networks were more versatile than the basic Elman nets in being able to model the dynamic behaviour of high order linear and nonlinear systems. |
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