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基于竞争方程的竞争型协同进化算法及应用

Optimizing Fuzzy Neural Network Using Gause Competitive Coevolution Algorithm
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摘要 协同进化算法是近年来针对遗传算法的不足而兴起的,还处于研究初步阶段。本文在竞争型协同进化的基础上,借鉴生态学中种群竞争的Gause竞争模型,提出了Gause竞争型协同进化模型及算法,并将该算法应用于模糊神经系统的辨识问题上。实验证明,该算法比标准遗传算法、典型竞争型协同进化算法和BP学习算法具有更好的全局收敛性和更快的收敛速度,它在一定程度上解决了标准遗传算法的不足。 Genetic Algorithm and Fuzzy Neural Networkare being the intense popular interests at present. A co - evolution algorithm isbrought forward to solve the premature convergence and speed up the convergenceof GA. This paper presents a new competitive co - evolution algorithm which offers advantages over traditional evolutionary algorithm based on the Gause competitive model in the field of ecology. To show the efficiency we apply it to optimize a fuzzy neural network.
出处 《微处理机》 2005年第1期33-36,共4页 Microprocessors
关键词 模拟进化算法 遗传算法 Gause竞争方程 竞争型协同进化 模糊神经网络 Evolutionary algorithm Genetic algorithm Gause competitive function Competitive co -evolution algorithm Fuzzy neural network
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参考文献4

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