基于小生境-遗传算法优化的新型BP模型

为解决传统BP神经网络模型易陷入局部极小点、网络结构不稳定、收敛速度慢等问题，提出了一个小生境遗传算法优化的BP神经网络模型。该网络模型借助BP神经网络的非线性映射和学习联想能力和小生境遗传算法的搜索能力，利用小生境遗传算法的选择、交叉、变异及小生境淘汰等操作，来对BP神经网络的初始权值和阈值进行优化，同时使用BP算法来训练该模型，从而有效地解决了网络初值不合理的问题，提高了网络收敛速度、稳定性。实验证明：与传统方法相比，该模型具有很强的可行性和有效性。

Optimized BP Neural Network Model Based on Niche-genetic Algorithm

According to shortcomings of BP neural network model, such as entrapment in local optimum, unstable network structure , slower convergence speed, etc. , an improved BP neural network model based on niche genetic algorithm I NGA-BP was presented. The proposed model firstly makes full use of the global searching ability of genetic algorithm and the nonlinear reflection ability and the association learning ability of BP neural network to optimize the initial connection weights and thresholds of the neural network by means of selection operation, crossover operation, mutation operation and niche pass, and then adopts BP algorithm to train network, which can effectively solve the questions of BP network about reasonable initial value and network misconvergence, and improve the convergence speed and the stability of network. The experiment results show that the model is more feasible and effective than the traditional methods.