计算机工程与应用2011，Vol.47Issue(28)：59-61,3.DOI:10.3778/j.issn.1002-8331.2011.28.015
用蛙跳算法优化RBF神经网络参数的研究
Using shuffled frog leaping algorithm to optimize the parameters of radial basis function neural network
摘要
Abstract
In allusion to being difficult to determine the parameters of Radial Basis Functions Neural Network(RBFNN), a new method on the parameters optimization of radial basis function neural network based on Shuffled Frog Leaping Algorithm (SFLA) is proposed.The parameters of the RBFNN compose a multidimensional vector which is regarded as parameters of SFLA to optimize.According to the fitness function,the feasible sampling space is searched for the global optima,further more,the SFLA has been improved.The simulation test on nonlinear function approximation shows that compared to Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) the new method has less mean square error and better approximation ability.关键词
蛙跳算法/径向基函数神经网络/非线性函数逼近/参数优化Key words
shuffled frog leaping algorithm/Radial Basis Functions Neural Network (RBFNN)/nonlinear function approximation parameters optimization分类
信息技术与安全科学引用本文复制引用
薛升翔,贾振红,杨杰,庞韶宁..用蛙跳算法优化RBF神经网络参数的研究[J].计算机工程与应用,2011,47(28):59-61,3.基金项目
科技部国际科技合作项目(No.2009DFA12870). （No.2009DFA12870）
