基于趋势检查法的遗传神经网络模型及工程应用

针对神经网络中模型可靠性问题,提出了趋势检查法的思路,采用评价指标中评价等级的影响趋势对模型进行检查,基本过程为不断调整模型参数、训练、趋势检查,直到获得最优模型。趋势检查法为一种通用方法,可用于任何基于先知经验方法的模型可靠性检查,为模型可靠性检查提供了一种新思路。对于神经网络学习样本贡献度不同的问题,采用样本加权的方法,对样本进行预处理,并将样本权值应用于神经网络的目标函数中,由此建立了加权神经网络目标函数。最后引入遗传算法来优化神经网络参数,建立了基于趋势检查法的遗传神经网络模型,并应用于实际工程中的围岩分类问题,结果表明该模型泛化能力强,具有较高的分类精度。

A genetic neural network model based on a trend examination method and engineering application

Aiming at the model’s reliability problem of a neural network, a trend examination method was presented to check the model’s reliability. It checked the model through the influence trend of evaluation index to evaluation grade. The process of the method was  incessantly adjusting the model’s parameter, training, and trend examination, until  the best model was obtained. This method  presented  a new idea and can be used in any problems of model’s reliability examination based on the foreknowable experience method. To the problem of the contribution difference of samples, the method of weighted samples was used to preprocess the samples and the samples weight was used in the objective function of the neural network. Finally, a genetic algorithm was adopted to optimize the parameter of the  neural network and a GA ANN model based on the trend examination method was established. The improved model was applied to practical engineering about surrounding rock classification and the results showed that this method can improve the neural network generalization ability and prediction accuracy.