Spatio-Temporal Modeling of Nonlinear Distributed Parameter Systems

by Li, Han-xiong; Qi, Chenkun

• ISBN13: 9789400707405
• ISBN10: 9400707401
• Format: Hardcover
• Copyright: 2011-02-28
• Publisher: Springer Verlag

Summary

#xA0;The purpose of this#xA0;volume is to provide a brief review of the previous work on model reduction and identification of DPS, and develop new spatio-temporal models and their relevant identification approaches. #xA0;In this book, a systematic overview and classification on the modeling of DPS is presented first, which includes model reduction, parameter estimation and system identification. Next, a class of block-oriented nonlinear systems in traditional LPS is extended to DPS, which results in the spatio-temporal Wiener and Hammerstein systems and their identification methods. Then, the traditional Volterra model is extended to DPS, which results in the spatio-temporal Volterra model and its identification algorithm. All these methods are based on linear time/space separation. Sometimes, the nonlinear time/space separation can play a better role in modeling of very complex process. Thus, a nonlinear time/space separation based neural modeling is also presented for a class of DPS with more complicated dynamics. Finally, all these modeling approaches are successfully applied to industrial thermal processes, including a catalytic rod, a packed-bed reactor and a snap curing oven.#xA0;The work is presented giving a unified view from time/space separation. The book also illustrates applications to thermal processes in the electronics packaging and chemical industry.#xA0;This volume#xA0;assumes a basic knowledge about distributed parameter systems, system modeling and identification. It is intended for researchers, graduate students and engineers interested in distributed parameter systems, nonlinear systems, and process modeling and control.