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

针对径向基函数(Radial Basis Functions,RBF)神经网络结构参数确定问题,提出了一种基于蛙跳算法优化RBF神经网络参数的新方法。将RBF神经网络参数组成一个多维向量,作为蛙跳算法中的参数进行优化。以适应度函数为标准,在可行解空间中搜索最优解,并对蛙跳算法进行了改进。非线性函数逼近实验结果表明,该优化算法相对标准遗传优化算法、粒子群优化算法有较小的均方误差,具有更好的逼近能力。

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 Algo- rithm（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 Ge- netic Algorithm（GA） and Particle Swarm Optimization（PSO） the new method has less mean square error and better approximation ability.