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一种优化模糊神经网络的多目标微粒群算法 被引量：9

Fuzzy Neural Network Optimization by a Multi-Objective Particle Swarm Optimization Algorithm
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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· 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.
作者 马铭 周春光 张利彪 马捷
机构地区 北华大学信息管理中心 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
出处 《计算机研究与发展》 EI CSCD 北大核心 2006年第12期2104-2109,共6页 Journal of Computer Research and Development
基金 国家自然科学基金重点项目(60433020) 国家"九八五"工程"计算与软件科学科技创新平台"基金项目 教育部"符号计算与知识工程"重点实验室基金项目(02090)~~
关键词 模糊神经网络 微粒群算法 多目标优化 fuzzy neural network particle swarm optimization multi-objective optimization
分类号 TP183 [自动化与计算机技术—控制理论与控制工程]
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参考文献14

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二级参考文献17

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共引文献5

同被引文献178

引证文献9

二级引证文献45

计算机研究与发展
2006年 第12期

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