中文说明：应用背景小波神经网络采用传统BP算法，存在收敛速度慢和易陷入局部极小值两个突出弱点。本文建立了基于遗传算法的小波神经网络股票预测模型GA-WNN。该模型结合了遗传算法的全局优化搜索能力以及小波神经网络良好的时频局部特性。运用MATLAB对拟合和预测过程进行仿真。结果表明，该模型能有效地提高预测精度，避免了BP算法固有缺陷关键技术遗传算法优化神经网络的matlab仿真代码。成功调试之后得到预测模型GA-WNN。结合了两个的优质特性得到最优解的matlab代码。
English Description：
Application backgroundWavelet neural network using the traditional BP algorithm, the existence of slow convergence speed and easy to fall into local minimum of two prominent weaknesses. In this paper, a genetic algorithm based on the wavelet neural network stock forecasting model GA-WNN. The model combines the global optimization searching ability of genetic algorithm and the time-frequency local characteristic of wavelet neural network. Simulation of fitting and forecasting process using MATLAB. The results show that the proposed model can effectively improve the prediction accuracy and avoid the inherent defects of BP algorithm.Key TechnologyGenetic algorithm optimization neural network matlab simulation code. After successful commissioning, the prediction model of GA-WNN. The matlab code of the optimal solution is obtained by combining two quality characteristics.