基于免疫聚类和遗传算法的RBF网络设计方法

基于人工免疫机制和遗传算法,提出了一种训练径向基函数(RBF)网络的混合算法.该算法采用了一种可以实现数据聚类的人工免疫机制根据输入数据集合自适应地确定RBF网络隐层中心的数量和初始位置;采用遗传算法训练RBF网络,能够使优化过程趋于全局最优.将该方法用于多用户检测问题的实验结果表明,采用这种混合算法训练的RBF网络结构精简,具有很好的抗多址干扰的性能.

A RBF Designing Method Based on Immune Clustering and Genetic Algorithm

Based on artificial immunology and genetic algorithm, a hybrid algorithm to design the RBF network is proposed. An artificial immune mechanism for data clustering is used to adaptively specify the amount and initial positions of the RBF centers according to input data set; then RBF network is then trained with genetic algorithm that makes the training process tend to global optima. The application of the algorithm in multiuser detection problems demonstrates that the RBF network trained with the algorithm is concise in structure and has good anti-MAI performance.