摘要
Aimed at studying normali zed radial basis function network (NRBFN), this paper introduces the subtractiv e clustering based on a mountain function to construct the initial structure of NR BFN, adopts singular value decomposition (SVD) to analyze the relationship betwe en neural nodes of the hidden layer and singular values, cumulative contribution ratio, index vector, and optimizes the structure of NRBFN. Finally, simulation and performance comparison show that the algorithm is feasible and effective.
针对归一化 RBF网络 ,利用基于山峰函数的减法聚类算法构造归一化 RBF网络的初始结构 ,采用奇异值分解 ( SVD)算法分析了网络隐含层节点与奇异值、累积贡献率以及索引向量的关系 ,并对归一化 RBF网络的结构进行了优化。最后 ,对该优化策略的可行性和有效性进行了仿真验证和性能比较。
