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一种优化模糊神经网络的多目标微粒群算法
引用本文:马 铭, 周春光, 张利彪, 马 捷. 一种优化模糊神经网络的多目标微粒群算法[J]. 计算机研究与发展, 2006, 43(12): 2104-2109.
作者姓名:马铭  周春光  张利彪  马捷
作者单位:1(北华大学信息管理中心 吉林 132013) 2(吉林大学计算机科学与技术学院教育部符号计算与知识工程重点实验室 长春 130012) (mam@mail.edu.cn)
基金项目:国家自然科学基金;面向21世纪教育振兴行动计划(985计划);教育部重点实验室基金
摘    要:模糊神经网络优化是一个多目标优化问题.通过对模糊神经网络和微粒群算法的深入分析,提出了一种多目标微粒群算法.在算法中将网络的精确性和复杂性分别作为目标进行优化,再用一种启发性分量加权均值法来选取个体极值和全局极值.算法能够引导粒子较快地向非劣最优解区域移动并最终获得多个非劣最优解,为模糊神经网络的精确性和复杂性的折中寻优问题提供了一种解决方法.茶味觉信号识别的仿真实验验证了该算法的有效性.

关 键 词:模糊神经网络  微粒群算法  多目标优化
收稿时间:2005-11-19
修稿时间:2005-11-192006-03-01

Fuzzy Neural Network Optimization by a Multi-Objective Particle Swarm Optimization Algorithm
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.
Authors:Ma Ming  Zhou Chunguang  Zhang Libiao  Ma Jie
Affiliation: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)
Abstract: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 nondominated solutions. Numerical simulations for taste identification of tea show the effectiveness of the proposed algorithm.
Keywords:fuzzy neural network   particle swarm optimization   multi-objective optimization
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