信息与控制2016，Vol.45Issue(5)：627-633,7.DOI:10.13976/j.cnki.xk.2016.0627
基于极限学习离散过程神经网络的示功图识别
Indicator Diagram Recognition Based on Extreme Learning Discrete Process Neural Networks
摘要
Abstract
When a pumping well indicator diagram is diagnosed by traditional artificial neural networks,the model is limited by the synchronous instantaneous input.It cannot reflect the cumulative time effect for continuous input signal and has low diagnostic accuracy.Aiming at solving this problem,we propose an extreme learning discrete process neural network.Three-spline numerical integration is applied to deal with the aggregation of discrete samples and weights in the time-domain.An extreme leaning algorithm is applied to the model′s training and converts it to a least squares problem.The Moore-Penrose generalized inverse matrix and a hid-den layer output matrix are used to compute the output weight.The training speed is enhanced.When the model is used to diagnose five common statuses in the indicator diagram,the discrete time sequence data on the displacement and load are taken as the model input.The experimental results show that the method has higher identification accuracy and faster learning speed than other process neural network.关键词
示功图离散过程神经元网络/极限学习/Moore-Penrose广义逆/网络训练Key words
indicator diagram/discrete process neural network/extreme learning/Moore-Penrose generalized inverse/network training分类
信息技术与安全科学引用本文复制引用
刘志刚,许少华,李盼池,赵云龙..基于极限学习离散过程神经网络的示功图识别[J].信息与控制,2016,45(5):627-633,7.基金项目
国家自然科学基金资助项目（61170132）；黑龙江省教育厅基金资助项目 （）
