Training Fuzzy Neural Network via Multiobjective Optimization for Nonlinear Systems Identification
The design of a fuzzy neural network (FNN) has long been a challenging problem since most methods rely on approximation error to train an FNN, which may easily result in overfitting phenomenon to degrade the generalization performance. To improve the generalization performance, an FNN with a multiob...
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| Published in: | IEEE transactions on fuzzy systems Vol. 30; no. 9; pp. 3574 - 3588 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
IEEE
01.09.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1063-6706, 1941-0034 |
| Online Access: | Get full text |
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| Abstract | The design of a fuzzy neural network (FNN) has long been a challenging problem since most methods rely on approximation error to train an FNN, which may easily result in overfitting phenomenon to degrade the generalization performance. To improve the generalization performance, an FNN with a multiobjective optimization algorithm (MOO-FNN) is proposed in this article. First, the multilevel learning objectives are designed around the generalization performance to guide the training process of an FNN. Then, the method utilizes the approximation error, the structure complexity, and the output smoothness indicators instead of a single indicator to improve the evaluation accuracy of generalization performance. Second, an MOO algorithm with continuous-discrete variables is developed to optimize the FNN. Then, MOO is able to use a novel particle update method to adjust both the structure and parameters rather than adjusting them separately, thereby achieving suitable generalization performance of the FNN. Third, the convergence of MOO-FNN is analyzed in detail to guarantee its successful applications. Finally, the experimental studies of MOO-FNN have been performed on model identification of nonlinear systems to verify the effectiveness. The results illustrate that MOO-FNN has a significant improvement over some state-of-the-art algorithms. |
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| AbstractList | The design of a fuzzy neural network (FNN) has long been a challenging problem since most methods rely on approximation error to train an FNN, which may easily result in overfitting phenomenon to degrade the generalization performance. To improve the generalization performance, an FNN with a multiobjective optimization algorithm (MOO-FNN) is proposed in this article. First, the multilevel learning objectives are designed around the generalization performance to guide the training process of an FNN. Then, the method utilizes the approximation error, the structure complexity, and the output smoothness indicators instead of a single indicator to improve the evaluation accuracy of generalization performance. Second, an MOO algorithm with continuous–discrete variables is developed to optimize the FNN. Then, MOO is able to use a novel particle update method to adjust both the structure and parameters rather than adjusting them separately, thereby achieving suitable generalization performance of the FNN. Third, the convergence of MOO-FNN is analyzed in detail to guarantee its successful applications. Finally, the experimental studies of MOO-FNN have been performed on model identification of nonlinear systems to verify the effectiveness. The results illustrate that MOO-FNN has a significant improvement over some state-of-the-art algorithms. |
| Author | Wu, Xiaolong Yang, Hongyan Qiao, Junfei Han, Honggui Sun, Chenxuan |
| Author_xml | – sequence: 1 givenname: Honggui orcidid: 0000-0001-5617-4075 surname: Han fullname: Han, Honggui email: rechardhan@sina.com organization: Faculty of Information Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System and Engineering Research Center of Digital Community Ministry of Education, Beijing University of Technology, Beijing, China – sequence: 2 givenname: Chenxuan surname: Sun fullname: Sun, Chenxuan organization: Faculty of Information Technology and Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, China – sequence: 3 givenname: Xiaolong orcidid: 0000-0002-7713-1995 surname: Wu fullname: Wu, Xiaolong organization: Faculty of Information Technology and Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, China – sequence: 4 givenname: Hongyan surname: Yang fullname: Yang, Hongyan organization: Faculty of Information Technology and Engineering Research Center of Digital Community, Beijing University of Technology, Beijing, China – sequence: 5 givenname: Junfei surname: Qiao fullname: Qiao, Junfei organization: Faculty of Information Technology and Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, China |
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| Snippet | The design of a fuzzy neural network (FNN) has long been a challenging problem since most methods rely on approximation error to train an FNN, which may easily... |
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| SubjectTerms | Algorithms Approximation Approximation algorithms Artificial neural networks Complexity theory Continuity (mathematics) Convergence Fuzzy control Fuzzy logic fuzzy neural network (FNN) Fuzzy neural networks generalization performance Machine learning multiobjective particle swarm optimization (PSO) algorithm Multiple objective analysis Neural networks Nonlinear systems Optimization Smoothness System effectiveness Training |
| Title | Training Fuzzy Neural Network via Multiobjective Optimization for Nonlinear Systems Identification |
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