Combined algorithms for training RBF neural networks based on genetic algorithms and gradient descent
基于遗传算法和梯度下降的RBF神经网络组合训练方法

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. Genetic Algorithm (GA) based methods can get rid of the problem of local optima, but they are not very effective to refine an existent good solution. For resolving these problems, in this paper we proposed a new algorithm. The experimental results show the algorithm performs well, and it is better than both Gradient descent algorithm and genetic algorithm.