基于免疫遗传算法和梯度下降的RBF网组合训练方法

免疫遗传算法除了具有简单遗传算法的全局寻优能力外,还具有免疫记忆、免疫调节及多样性保持功能。梯度下降算法训练神经网络收敛速度慢,容易陷入局部最优,且受初始值的影响较大。本文综合两种方法的优点,提出一种用免疫遗传算法结合梯度下降算法的组合训练方法,用于RBF网的训练,并通过实验证明所提出的组合算法比简单遗传算法结合梯度下降组合算法的速度更快并且最终误差更小。

Combined Algorithm for Training RBF Neural Network Based on Immune Genetic Algorithm and Gradient Descent

Besides the ability of stochastic global searching of simple genetic algorithm（SGA）,the immune genetic algorithm（IGA） draws into the mechanisms which exist in biological immune system such as immune memory,immune regulation and antibody diversity.The deficiencies of gradient descent method include the slow speed of convergence,the problem of local minima and the great influence of initial parameters on the performance of the network.This paper proposes a new algorithm based on the IGA combined with gradient descent,and applies the new algorithm in the training of RBF network.The experimental results show the algorithm performs well,and it performs better than SGA combined with gradient descent.