误差反向传播神经网络优化方法研究

Research on optimization method of error backpropagation neural network

传统误差反向传播(BackPropagation,BP)神经网络虽然具有较强的拟合能力,但其预测误差受到学习率和权值更新方式的影响较大。如果学习率选择不当,网络的权值更新可能陷入局部最优,从而影响整体的优化能力。为了解决这些问题,通过优化权值更新、调整学习率和数据集预处理等方法,文章对传统BP网络算法进行了改进。仿真结果表明,优化后的BP神经网络具有更低的均方误差,并能更快速、稳定地实现收敛。

Although traditional error backpropagation neural networks have strong fitting ability,their prediction error is greatly affected by the learning rate and weight update method.If the learning rate is not properly selected,the weight updates of the network may fall into local optima,thereby affecting the overall optimization ability.To address these issues,the article improved the traditional BP network algorithm by optimizing weight updates,adjusting learning rates,and preprocessing the dataset.The simulation results show that the optimized BP neural network has lower mean square error and can achieve convergence more quickly and stably.