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.
General
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