Abstract:
A new training method is presented for RBF neural network. Genetic algorithm is used to optimize the centers and widths of RBF. Recurrent least square method is used to train the weights between hidden layer and output layer. The approach is used in the approximation of nonlinear function. And the result indicates it’s effective.
Key words:
RBF neural network,
Genetic algorithm,
Recurrent least square method
摘要： 提出了一种新的RBF神经网络的训练方法，采用遗传算法对RBF神经网络的隐层中心值和宽度进行了优化，用递推最小二乘法训练隐层和输出层之间的权值。在对非线性函数进行逼近的仿真中，验证了该算法的有效性。
关键词:
径向基函数神经网络,
遗传算法,
递推最小二乘法
ZHAO Zhigang; SHAN Xiaohong. Optimization Approach Based on Genetic Algorithm
for RBF Neural Network
[J]. Computer Engineering, 2007, 33(06): 211-212.
赵志刚;单晓虹. 一种基于遗传算法的RBF神经网络优化方法[J]. 计算机工程, 2007, 33(06): 211-212.