径向基函数神经网络的一种两级学习方法

A two-level learning hierarchy for the radial basis function networks

中文关键词  径向基网络  两级学习  建模  泛化能力

英文关键词  radial basis function networks  two-level learning hierarchy  modeling  generalization ability

中文摘要

      建立RBF(radial basis function)神经网络模型关键在于确定网络隐中心向量、基宽度参数和隐节点数. 为设计结构简单, 且具有良好泛化性能径向基网络结构, 本文提出了一种RBF网络的两级学习新设计方法. 该方法在下级由正则化正交最小二乘法与D-最优试验设计结合算法自动构建结构节俭的RBF网络模型; 在上级通过粒子群优化算法优选结合算法中影响网络泛化性能的3个学习参数, 即基宽度参数、正则化系数和D-最优代价系数的最佳参数组合. 仿真实例表明了该方法的有效性.

英文摘要

      The key to construct a radial basis function(RBF) network is to select reasonable hidden center vectors, RBF width and hidden node number. In order to design a RBF network with parsimonious structure and good generalization, a new two-level learning hierarchy for designing RBF networks is proposed. At the lower level in this method, a parsimonious RBF model is constructed by an integrated algorithm (ROLS+D-opt) which combines regularized orthogonal least squares (ROLS) with D-optimality experimental design (D-opt). At the upper level, particle swarm optimization (PSO) is used to search the optimal combination of three important learning parameters, i.e., the RBF width, the regularized parameter and D-optimality weight parameter, which influence the network’s generalization ability. Simulation results show the effectiveness of the proposed method.