一种基于新型RBF网络的侦察测向系统

针对雷达侦察系统的到达角(AOA)测量问题,提出了一种新型的基于两级径向基函数(RBF)网络的侦察测向系统。根据RBF网络的特点,本文采用了一种基于免疫算法的混合方法确定RBF网络测向系统的参数。该方法利用免疫算法的全局搜索能力优化RBF网络隐层的结构和参数,并采用最小二乘算法计算RBF网络线性输出层的权值。计算机仿真表明,基于这种RBF网络的测向系统达到了很高的精度。     

A RBF Network Learning Scheme Using Immune Algorithm Based on Information Entropy

A hybrid learning method combining immune algorithm and least square method is proposed to design the radial basis function(RBF) networks. The immune algorithm based on information entropy is used to determine the structure and parameters of RBF nonlinear hidden layer, and weights of RBF linear output layer are computed with least square method. By introducing the diversity control and immune memory mechanism, the algorithm improves the efficiency and overcomes the immature problem in genetic algorithm. Computer simulations demonstrate that the RBF networks designed in this method have fast convergence speed with good performances.     

基于免疫算法的前向神经网络学习方法

提出了一种采用免疫算法训练多层前向神经网络的方法。该方法利用免疫算法训练前向神经网络,能够使网络优化过程趋于全局最优。利用基于遗传策略的聚类机制确定前向神经网络的初始权值,增加了网络训练算法收敛于全局最优的概率。将这种神经网络用于雷达模拟调制信号的调制方式识别的仿真结果表明,采用该算法设计的前向神经网络达到了较高的性能。     

基于改进免疫算法的两级径向基函数网络学习方法

结合改进的免疫算法和最小二乘法，提出了一种设计径向基函数(RBF)网络的两级学习方法。该方法利用免疫算法确定RBF网络隐层的非线性参数，能够有效克服进化算法的未成熟收敛现象。改进的免疫算法针对RBF网络的特点，采用基于矢量距离的亲和度计算方法，克服了原有基于信息熵计算方法存在的计算复杂、参数难于确定的缺陷。将这种方法设计的RBF网络用于Mackey-Glass混沌序列预测的仿真实验证明了该方法的有效性。     

ERBF network with immune clustering

Based on immune clustering and evolutionary programming(EP), a hybrid algorithm to train the RBF network is proposed. An immune fuzzy C-means clustering algorithm (IFCM) is used to adaptively specify the amount and initial positions of the RBF centers according to input data set; then the RBF network is trained with EP that tends to global optima. The application of the hybrid algorithm in multiuser detection problem demonstrates that the RBF network trained with the algorithm has simple network structure with good generalization ability.