Batch Process Modeling with Multilayer Recurrent Fuzzy Neural Network

摘要：

A multilayer recurrent fuzzy neural network (MRFNN) with local feedbacks is proposed for batch process modeling. The local feedbacks in the membership layer and the rule layer introduce dynamics into the network.Learning algorithm of MRFNN include structure learning and parameters learning. By structure learning, the membership and rule layers are automatically constructed. Modified chaotic search (CS) and least square estimation (LSE) are combined for parameters learning,where CS is for tuning the premise parameters including feedback coefficients of the membership and rule layers, and LSE is for updating the consequent coefficients accordingly. Results of simulation on nonlinear function identification and a batch reactor reveal that the proposed MRFNN can capture the nonlinear and time-varying characteristics of dynamic system well.

关键词： Batch process recurrent fuzzy neural networks chaotic search modeling

作者: He Liu Dao Huang

作者单位: School of Information Science and Engineering East China University of Science and Technology Shanghai, P. R. China

会议类型: 国际会议

会议名称: 2007 Conference on Systems Science, Management Science and System Dynamics(第二届系统科学、管理科学与系统动力学国际会议)

会议地点: 上海

会议语种:英文

页码: 1351-1358

在线出版日期: 2007-10-19（万方平台首次上网日期，不代表论文的发表时间）