1 - 8 of 8 Chapters
[This chapter is an introduction of the book. Starting from typical examples of distributed parameter systems (DPS) encountered in the real-world, it briefly introduces the background and the motivation of the research, and finally the contributions and organization of the book.]
[This chapter provides a systematic overview of the distributed parameter system (DPS) modeling and its classification. Three different problems in DPS modeling are discussed, which includes model reduction for known DPS, parameter estimation for DPS, and system identification for unknown DPS....
[For Wiener distributed parameter systems (DPS), a spatio-temporal Wiener model (a linear DPS followed by a static nonlinearity) is constructed in this chapter. After the time/space separation, it can be represented by the traditional Wiener system with a set of spatial basis functions. To...
[A spatio-temporal Hammerstein modeling approach is presented in this chapter. To model the nonlinear distributed parameter system (DPS), a spatio-temporal Hammerstein model (a static nonlinearity followed by a linear DPS) is constructed. After the time/space separation, it can be represented by...
[A multi-channel spatio-temporal Hammerstein modeling approach is presented in this chapter. As a special case of the model described in Chapter 4, a spatio-temporal Hammerstein model is constructed with a static nonlinearity followed by a linear spatio-temporal kernel. When the model structure...
[To model the nonlinear distributed parameter system (DPS), a spatio-temporal Volterra model is presented with a series of spatio-temporal kernels. It can be considered as a nonlinear generalization of Green’s function or a spatial extension of the traditional Volterra model. To obtain a...
[A nonlinear principal component analysis (NL-PCA) based neural modeling approach is presented for a lower-order or more accurate solution for nonlinear distributed parameter systems (DPS). One NL-PCA network is trained for the nonlinear dimension reduction and the nonlinear time/space...
[This chapter summarizes all the methods introduced in the book, and discusses future challenges in this area.]
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