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一种基于遗传算法的模糊神经网络结构和参数优化
FUZZY NEURAL NETWORK STRUCTURE AND ITS PARAME TERS OPTIMIZATION BY GENETIC ALGORITHM
【摘要】 提出一种基于遗传算法的三阶段优化策略.在给定初始参数基础上,利用基于十进制编码的遗传算法实现模糊神经网络的结构优化,用基于二进制编码的遗传算法实现模糊神经网络的参数优化.仿真结果表明上述优化策略是有效的
【Abstract】 An optimization strategy with three steps is presented. Based on the initial parameters that have been determined in the first step, the structure of a fuzzy neural network is optimized by using a genetic algorithm with the decimal coding scheme, and the parameters are optimized with the binary coding scheme. Numerical simulations show that the optimization strategy is available.
【关键词】 模糊神经网络;
遗传算法;
结构优化;
参数优化;
【Key words】 fuzzy nearal network; genetic algorithm; structure optimization; parametar optimization;
【Key words】 fuzzy nearal network; genetic algorithm; structure optimization; parametar optimization;
【基金】 广东省自然科学基金
- 【文献出处】 华南理工大学学报(自然科学版) ,JOURNAL OF SOUTH CHINA UNIVERSITY OF TECHNOLOGY(NATURAL SCIENCE) , 编辑部邮箱 ,1999年01期
- 【分类号】TP18
- 【被引频次】43
- 【下载频次】441