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Intelligent Systems, Control and Automation: Science and Eng #50: Spatio-Temporal Modeling of Nonlinear Distributed Parameter Systems

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Intelligent Systems, Control and Automation: Science and Eng #50: Spatio-Temporal Modeling of Nonlinear Distributed Parameter Systems Cover

 

Synopses & Reviews

Publisher Comments:

The purpose of this volume is to provide a brief review of the previous work on model reduction and identifi cation of distributed parameter systems (DPS), and develop new spatio-temporal models and their relevant identifi cation approaches. In this book, a systematic overview and classifi cation on the modeling of DPS is presented fi rst, which includes model reduction, parameter estimation and system identifi cation. Next, a class of block-oriented nonlinear systems in traditional lumped parameter systems (LPS) is extended to DPS, which results in the spatio-temporal Wiener and Hammerstein systems and their identifi cation 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 processes. 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. The work is presented giving a unifi ed view from time/space separation. The book also illustrates applications to thermal processes in the electronics packaging and chemical industry. This volume assumes a basic knowledge about distributed parameter systems, system modeling and identifi cation. It is intended for researchers, graduate students and engineers interested in distributed parameter systems, nonlinear systems, and process modeling and control.

Synopsis:

This volume provides a brief review of the previous work on model reduction and identification of DPS, and develops new spatio-temporal models and their relevant identification approaches. All modeling approaches are applied to industrial thermal processes.

Table of Contents

Preface; List of Figures; List of Tables; Abbreviations; 1 Introduction; 1.1 Background; 1.1.1 Examples of distributed parameter processes; 1.1.2 Motivation; 1.2 Contributions and organization of the book; 1.3 References; 2 Modeling of Distributed Parameter Systems: Overview and Classification; 2.1 Introduction; 2.2 White-box modeling: model reduction for known DPS; 2.2.1 Eigenfunction method; 2.2.2 Green's function method; 2.2.3 Finite difference method; 2.2.4 Weighted residual method; 2.2.4.1 Classification based on weighting functions; 2.2.4.2 Classification based on basis functions; 2.2.5 Comparison studies of spectral and KL method; 2.3 Grey-box modeling: parameter estimation for partly known DPS; 2.3.1 FDM based estimation; 2.3.2 FEM based estimation; 2.3.3 Spectral based estimation; 2.3.4 KL based estimation; 2.4 Black-box modeling: system identification for unknown DPS; 2.4.1 Green's function based identification; 2.4.2 FDM based identification; 2.4.3 FEM based identification; 2.4.4 Spectral based identification; 2.4.5 KL based identification; 2.4.6 Comparison studies of neural spectral and neural KL method; 2.5 Concluding remarks; 2.6 References; 3 Spatio-Temporal Modeling for Wiener Distributed Parameter Systems; 3.1 Introduction; 3.2 Wiener distributed parameter system; 3.3 Spatio-temporal Wiener modeling methodology; 3.4 Karhunen-Loève decomposition; 3.5 Wiener model identification; 3.5.1 Model parameterization; 3.5.2 Parameter estimation; 3.6 Simulation and experiment; 3.6.1 Catalytic rod; 3.6.2 Snap curing oven; 3.7 Summary; 3.8 References; 4 Spatio-Temporal Modeling for Hammerstein Distributed Parameter Systems; 4.1 Introduction; 4.2 Hammerstein distributed parameter system; 4.3 Spatio-temporal Hammerstein modeling methodology; 4.4 Karhunen-Loève decomposition; 4.5 Hammerstein model identification; 4.5.1 Model parameterization; 4.5.2 Structure selection; 4.5.3 Parameter estimation; 4.6 Simulation and experiment; 4.6.1 Catalytic rod; 4.6.2 Snap curing oven; 4.7 Summary; 4.8 References; 5 Multi-Channel Spatio-Temporal Modeling for Hammerstein Distributed Parameter Systems; 5.1 Introduction; 5.2 Hammerstein distributed parameter system; 5.3 Basic identification approach; 5.3.1 Basis function expansion; 5.3.2 Temporal modeling problem; 5.3.3 Least-squares estimation; 5.3.4 Singular value decomposition; 5.4 Multi-channel identification approach; 5.4.1 Motivation; 5.4.2 Multi-channel identification; 5.4.3 Convergence analysis; 5.5 Simulation and experiment; 5.5.1 Packed-bed reactor; 5.5.2 Snap curing oven; 5.6 Summary; 5.7 References; 6 Spatio-Temporal Volterra Modeling for a Class of Nonlinear DPS; 6.1 Introduction; 6.2 Spatio-temporal Volterra model; 6.3 Spatio-temporal modeling approach; 6.3.1 Time/space separation; 6.3.2 Temporal modeling problem; 6.3.3 Parameter estimation; 6.4 State space realization; 6.5 Convergence analysis; 6.6 Simulation and experiment; 6.6.1 Catalytic rod; 6.6.2 Snap curing oven; 6.7 Summary; 6.8 References; 7 Nonlinear Dimension Reduction based Neural Modeling for Nonlinear Complex DPS; 7.1 Introduction; 7.2 Nonlinear PCA based spatio-temporal modeling framework; 7.2.1 Modeling methodology; 7.2.2 Principal component analysis; 7.2.3 Nonlinear PCA for projection and reconstruction; 7.2.4 Dynamic modeling; 7.3 Nonlinear PCA based spatio-temporal modeling in neural system; 7.3.1 Neural network for nonlinear PCA; 7.3.2 Neural network for dynamic modeling; 7.4 Simulation and experiment; 7.4.1 Catalytic rod; 7.4.2 Snap curing oven; 7.5 Summary; 7.6 References; 8 Conclusions; 8.1 Conclusions; 8.2 References; Index.

Product Details

ISBN:
9789400707405
Author:
Li, Han-xiong
Publisher:
Springer
Author:
Li, Han-Xiong
Author:
Qi, Chenkun
Subject:
Applied
Subject:
DPS
Subject:
Control
Subject:
spatio-temporal modeling
Subject:
thermal processes
Subject:
time separation
Subject:
Mathematical Modeling and Industrial Mathematics
Subject:
Industrial Chemistry/Chemical Engineering
Subject:
Simulation and Modeling
Subject:
Simulation and Modeling <p>systematic review of the progress so far on modelling of distributed parameter systems; </p><p>unified view from the time/space separation to synthesize to different methods; </p><p>some new spatio-temporal  models and their id
Subject:
Mathematics-Applied
Copyright:
Edition Description:
2011
Series:
Intelligent Systems, Control and Automation: Science and Engineering
Series Volume:
50
Publication Date:
20110309
Binding:
HARDCOVER
Language:
English
Pages:
194
Dimensions:
235 x 155 mm 1000 gr

Related Subjects

Computers and Internet » Computers Reference » General
Computers and Internet » Personal Computers » General
History and Social Science » Geography » General
Reference » Science Reference » Technology
Science and Mathematics » Chemistry » General
Science and Mathematics » Electricity » General Electronics
Science and Mathematics » Mathematics » Applied
Science and Mathematics » Mathematics » Modeling

Intelligent Systems, Control and Automation: Science and Eng #50: Spatio-Temporal Modeling of Nonlinear Distributed Parameter Systems New Hardcover
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Product details 194 pages Springer - English 9789400707405 Reviews:
"Synopsis" by , This volume provides a brief review of the previous work on model reduction and identification of DPS, and develops new spatio-temporal models and their relevant identification approaches. All modeling approaches are applied to industrial thermal processes.
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