Modeling of nonlinear systems using the self-organizing fuzzy neural network with adaptive gradient algorithm
In this paper, a self-organizing fuzzy neural network with adaptive gradient algorithm (SOFNN-AGA) is proposed for nonlinear systems modeling. First, a potentiality of fuzzy rules (PFR) method is introduced by using the output of normalized layer and the error reduction ratio (ERR) in the training p...
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| Published in | Neurocomputing (Amsterdam) Vol. 266; pp. 566 - 578 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
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Elsevier B.V
29.11.2017
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| Subjects | |
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| Abstract | In this paper, a self-organizing fuzzy neural network with adaptive gradient algorithm (SOFNN-AGA) is proposed for nonlinear systems modeling. First, a potentiality of fuzzy rules (PFR) method is introduced by using the output of normalized layer and the error reduction ratio (ERR) in the training process. And a structure learning approach is developed to determine the network size based on PFR. Second, a novel adaptive gradient algorithm (AGA) with adaptive learning rate is designed to adjust the parameters of SOFNN-AGA. Moreover, a theoretical analysis on the convergence of SOFNN-AGA is given to show the efficiency in both fixed structure and self-organizing structure cases. Finally, to demonstrate the merits of SOFNN-AGA, simulation and experimental results of several benchmark problems and a real world application are examined for nonlinear systems modeling with comparisons against other existing methods. Some promising results are reported in this study, indicating that the proposed SOFNN-AGA performs better favorably in terms of both convergence speed and modeling accuracy. |
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| AbstractList | In this paper, a self-organizing fuzzy neural network with adaptive gradient algorithm (SOFNN-AGA) is proposed for nonlinear systems modeling. First, a potentiality of fuzzy rules (PFR) method is introduced by using the output of normalized layer and the error reduction ratio (ERR) in the training process. And a structure learning approach is developed to determine the network size based on PFR. Second, a novel adaptive gradient algorithm (AGA) with adaptive learning rate is designed to adjust the parameters of SOFNN-AGA. Moreover, a theoretical analysis on the convergence of SOFNN-AGA is given to show the efficiency in both fixed structure and self-organizing structure cases. Finally, to demonstrate the merits of SOFNN-AGA, simulation and experimental results of several benchmark problems and a real world application are examined for nonlinear systems modeling with comparisons against other existing methods. Some promising results are reported in this study, indicating that the proposed SOFNN-AGA performs better favorably in terms of both convergence speed and modeling accuracy. |
| Author | Qiao, Jun-Fei Han, Hong-Gui Lin, Zheng-Lai |
| Author_xml | – sequence: 1 givenname: Hong-Gui surname: Han fullname: Han, Hong-Gui email: Rechardhan@sina.com organization: Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China – sequence: 2 givenname: Zheng-Lai surname: Lin fullname: Lin, Zheng-Lai email: beigongdalin@sina.com organization: Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China – sequence: 3 givenname: Jun-Fei surname: Qiao fullname: Qiao, Jun-Fei email: isibox@sina.com organization: Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China |
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| Keywords | Nonlinear system modeling Fast convergence Computational efficiency Self-organizing fuzzy neural network Adaptive gradient algorithm |
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| SubjectTerms | Adaptive gradient algorithm Computational efficiency Fast convergence Nonlinear system modeling Self-organizing fuzzy neural network |
| Title | Modeling of nonlinear systems using the self-organizing fuzzy neural network with adaptive gradient algorithm |
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