摘要
提出一种基于减聚类、K-means算法及改进的粒子群优化(PSO)算法的径向基函数(RBF)神经网络混合学习算法.该算法首先使用减聚类确定隐层节点数和K-means初始聚类中心;然后通过K-means算法求取RBF网络所有参数,作为PSO的初始粒子群;为了提高PSO算法的收敛性和稳定性,对基本PSO算法进行了优化改进,最后使用改进的PSO算法训练RBF神经网络中的所有参数.对IRIS数据集分类识别的仿真结果表明,改进的混合算法具有更高的分类准确率和更好的稳定性.
This paper introduces a hybrid learning algorithm for Radial Basis Function neural network（RBFNN） based on subtractive clustering, K-means clustering and particle swarm optimization algorithm（PSO）. The algorithm can be used to determine the number of hidden layer nodes and initial clustering centers of K-means by using subtractive clustering; Then the initial particle swarm of PSO can be formed by K-means clustering algorithm. The basic PSO algorithm are optimized and developed to improving convergence and stability of the algorithm, and finally the improved PSO algorithm is used to train all the parameters of RBFNN. The simulation for IRIS data set classification problem is executed, the experiment results show that the improved hybrid algorithm has higher accuracy and better stability than several other popular methods.
出处
《计算机系统应用》
2013年第2期84-87,47,共5页
Computer Systems & Applications
