关键词: 减聚类算法, k-means算法, 径向基函数（RBF）神经网络, 梯度下降法

Abstract: Aiming at the low classification accuracy of network trained by traditional RBF neural networks learning algorithm, the traditional k-means algorithm has sensitivity to the initial clustering center. To solve these problems, an improved learning algorithm based on improved k-means algorithm is proposed. The new algorithm optimizes k-means algorithm with subtractive clustering algorithm to eliminate the clustering sensitivity, and constructs RBF neural networks with the optimized k-means algorithm. The simulation results demonstrate the practicability and the effectiveness of the new algorithm.

Key words: subtractive clustering algorithm, k-means algorithm, Radial Basis Function（RBF） neural network, gradient descent algorithm