文献类型：期刊文献

英文题名：Hybrid neural network predictor for distributed parameter system based on nonlinear dimension reduction

作者：Wang, Mengling[1];Qi, Chenkun[2];Yan, Huaicheng[1];Shi, Hongbo[1]

机构：[1]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai 200030, Peoples R China

年份：2016

卷号：171

起止页码：1591

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20154301422446);WOS:【SCI-EXPANDED(收录号:WOS:000364883900152)】；

基金：This work was supported by National Nature Science Foundation of China (No. 61203059, 61272064 and 61374140), the Fundamental Research Funds for the Central Universities (No. 22A201514048) and the Open Research fund for Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University.

语种：英文

外文关键词：Neural network; Nonlinear parabolic distributed parameter system; Nonlinear dimension reduction; Recursive algorithm

摘要：In this study, a hybrid neural network predictor is proposed to predict spatiotemporal dynamics of the nonlinear distributed parameter systems (DPSs) with unwanted disturbance or slow set point changes. First, a nonlinear principal component analysis (NL-PCA) network is designed to transform the high-dimensional spatiotemporal data into a low-dimensional time domain, which can better represent the nonlinearity of the system compared to the linear time/space separation method. Then the hybrid NN models are built to identify the low-dimensional temporal data. To capture the spatiotemporal dynamics of DPS, the four-step recursive algorithm is used to obtain the time-varying weights of the model, while the parameters of NN model does not need to online update. The simulations demonstrated show that the proposed approach can achieve a good performance on prediction with system slow time-varying dynamics. (C) 2015 Elsevier B.V. All rights reserved.