k-means-RBF集成神经网络在工业尾气检测中的应用

神经网络是工业尾气检测系统的一个重要组成部分. 为提高神经网络的预测精度和收敛速度, 建立k-means-RBF集成神经网络模型. 首先, 通过选取不同的径向基函数神经网络参数, 得到一组RBF神经网络; 然后, 利用k-means算法对生成的RBF神经网络进行聚类, 并筛选出各类中精度较高的神经网络; 最后, 通过简单平均法对筛选出的神经网络进行集成, 得到高性能的k-means-RBF集成神经网络模型. 为验证模型有效性, 搭建基于k-means-RBF集成神经网络模型的工业尾气检测系统进行验证. 结果表明, 与粒子群算法优化后的Back Propagation (PSO-BP)神经网络模型相比, k-means-RBF集成神经网络模型的平均预测精度提高78.27%, 收敛时间节省99.65%

k-means-RBF integrated neural network and its application in industrial exhaust emission detection

Neural network is very important in detection of industrial exhaust emission. In order to improve stability and prediction accuracy of neural network and reduce the training time of neural network, k-means-RBF integrated neural network model is established based on k-means algorithm. Firstly, a set of RBF neural networks are obtained by choosing different RBF network function parameters. Secondly, the neural network with higher precision is selected from each type. Finally, high performance integration RBF neural network is obtained through integrating RBF neural network using the simple average method. In order to verify the validity of the model, the developed neural network model is used on the industrial exhaust emission detection system. The results show that, compared with the Back Propagation (BP) neural network method optimized by particle swarm optimization (PSO), the average precision predicted by the proposed neural network increases by 78.27%, and the processing time is saved by 99.65%