改进型LM-BP神经网络在水质评价中的应用

Application of improved LM-BP neural network in water quality evaluation

为了克服传统BP神经网络在水质评价中的不足,利用双极性S型函数输出可为正或负的特性以及LM算法具有梯度下降法的全局特性和高斯-牛顿的局部特性,对BP神经网络法进行改进,提出了基于双极性S型函数的改进型LM-BP神经网络模型的水质评价方法。应用结果表明,将此网络模型应用于水质评价是有效的、可行的。与其他评价方法相比,评价结果更加客观、合理。改进后的算法收敛速度较快,预测精度很高,为水质评价提供了一种新方法。

To overcome the shortcomings of the traditional BP network, an improved LM-BP neural network method based on the bipolarity S function is proposed for water quality evaluation. The method has the advantage of the bipolarity S function, a positive or negative output, and the benefits of the Levenberg-Marquardt （LM） algorithm, both the global property of the Gradient Decent algorithm and the partial property of the Gauss Newton algorithm. The results show that this method is effective and feasible for the evaluation of water quality. Compared with other methods, its evaluation results are more objective and reasonable. As a new water quality evaluation method, the improved LM-BP network method has high prediction accuracy and a high convergence rate.