基于遗传算法的BP神经网络电子系统状态预测方法研究

BP神经网络是一种应用面较广的神经网络,但存在明显缺陷:学习收敛速度慢,易陷入局部极小。遗传算法具有良好的搜索全局最优解的能力。为了提高BP神经网络预测模型在状态预测中的准确性,提出了一种基于遗传算法优化BP神经网络的状态预测方法.利用遗传算法优化BP神经网络的权值和阈值,然后训练BP神经网络预测模型以求得最优解,并将该预测方法应用到Buck输出电压平均值进行有效性验证。仿真结果表明,改进后方法具有较好的非线性拟合能力和更高的预测准确性。

Research on BP neural network state prediction method for electronic system based on genetic algorithm

BP neural network is a neural network with a wide range of applications, but there are obvious defects: slow convergence and easy to fall into local minima. The genetic algorithm has the ability to search the global optimal solution with a good prediction model. In order to improve the accuracy in the prediction of BP neural network, a prediction method of genetic algorithm optimization based on BP neural network is proposed. Using genetic algorithm to optimize BP neural network weights and thresholds, and then training the BP neural network prediction model to obtain the optimal solution, and the prediction method is applied to the Buck output voltage average validated. The simulation results show that the improved method has good non linear fitting capability and higher the accuracy of prediction.