基于遗传学习算法和BP算法的神经网络在矿坑涌水量计算中的应用

本文采用遗传学习算法和误差反向传播算法 (BP)相结合的混合算法来训练前馈人工神经网络 (BPN) ,即先用遗传学习算法进行全局训练 ,再用BP算法进行精确训练 ,使网络收敛速度加快和避免局部极小 .作为实例 ,本文将该方法运用于多维时序问题 .根据山东省黑旺铁矿的矿坑充水条件建立了一个网络 ,以矿坑充水的各种控制因素相关资料作为样本 ,对网络进行训练并用训练好的网络预测矿坑涌水量 .网络的训练速度及预测结果表明 ,该算法收敛速度较快 ,预测精度很高 ,为矿坑涌水量预报提供了一种新思路和新方法 .

基于多层前馈神经网络算法的房价预测模型

房地产泡沫问题是影响人民生活质量的一大难题,房价的持续上涨给人民增加了巨大的生活成本。因此,建立准确有效的房价预测模型,对于制定有效政策,精准调控房价,提高人民生活质量具有重要意义。文章主要通过分析住房的需求、供给、地理空间几大方面,得出影响房价的三个主要因素。采用基于遗传算法修正的BP(多层前馈)神经网络算法,利用2012~2019年无锡市三个主要影响因素的数据,建立了基于BP神经网络算法的房价预测模型。

ANN Model and Learning Algorithm in Fault Diagnosis for FMS

The fault diagnosis model for FMS based on multi layer feedforward neural networks was discussed An improved BP algorithm,the tactic of initial value selection based on genetic algorithm and the method of network structure optimization were presented for training this model ANN(artificial neural network)fault diagnosis model for the robot in FMS was made by the new algorithm The result is superior to the rtaditional algorithm

Neural network fault diagnosis method optimization with rough set and genetic algorithms

Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. The neural network nodes of the input layer can be calculated and simplified through rough sets theory; The neural network nodes of the middle layer are designed through genetic algorithms training; the neural network bottom-up weights and bias are obtained finally through the combination of genetic algorithms and BP algorithms. The analysis in this paper illustrates that the optimization method can improve the performance of the neural network fault diagnosis method greatly.