Ma Ming, Zhou Chunguang, Zhang Libiao, Ma Jie. Fuzzy Neural Network Optimization by a Multi-Objective Particle Swarm Optimization Algorithm[J]. Journal of Computer Research and Development, 2006, 43(12): 2104-2109.
Citation:
Ma Ming, Zhou Chunguang, Zhang Libiao, Ma Jie. Fuzzy Neural Network Optimization by a Multi-Objective Particle Swarm Optimization Algorithm[J]. Journal of Computer Research and Development, 2006, 43(12): 2104-2109.
Ma Ming, Zhou Chunguang, Zhang Libiao, Ma Jie. Fuzzy Neural Network Optimization by a Multi-Objective Particle Swarm Optimization Algorithm[J]. Journal of Computer Research and Development, 2006, 43(12): 2104-2109.
Citation:
Ma Ming, Zhou Chunguang, Zhang Libiao, Ma Jie. Fuzzy Neural Network Optimization by a Multi-Objective Particle Swarm Optimization Algorithm[J]. Journal of Computer Research and Development, 2006, 43(12): 2104-2109.
1(Information Management Center, Beihua University, Jilin 132013) 2(Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry of China, College of Computer Science and Technology, Jilin University, Changchun 130012)
Designing a set of fuzzy neural networks can be considered as solving a multi-objective optimization problem. In the problem, performance and complexity are two conflicting criteria. An algorithm for solving the multi objective optimization problem is presented based on particle swarm optimization through the improvement of the selection manner for global and individual extremum. The search for the Pareto optimal set of fuzzy neural networks optimization problems is performed, and a tradeoff between accuracy and complexity of fuzzy neural networks is clearly shown by obtaining non-dominated solutions. Numerical simulations for taste identification of tea show the effectiveness of the proposed algorithm.