NDLI: Low-dimensional approximation strategy for a class of nonlinear distributed parameter systems

Content Provider	IEEE Xplore Digital Library
Author	Mian Jiang Hua Deng
Copyright Year	2011
Description	Author affiliation: School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China (Mian Jiang; Hua Deng)
Abstract	A new low-dimensional approximation strategy is proposed for a class of nonlinear distributed parameter systems (DPS) with nonlinear uncertainties. Firstly, spectral method is used to obtain spectral based model with nominal nonlinear terms of DPS. After neglecting the nonlinear terms of the spectral based model, balanced truncation model reduction is carried out to obtain a balanced transform matrix and reduced linear terms. Lastly, a low-dimensional hybrid intelligent neural network model is used to identify spectral based model, while neural network is trained to approximate uncertain nonlinear terms. The simulation for spatio-temperature evolution of catalytic rod are presented to show the effectiveness of this low dimensional approximation strategy.
Starting Page	385
Ending Page	388
File Size	705289
Page Count	4
File Format	PDF
ISBN	9781424494361
e-ISBN	9781424494392
DOI	10.1109/MACE.2011.5986940
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hybrid intelligent system identification Nonlinear distributed parameter systems Spectral method Process control Balanced truncation model reduction Reduced order systems Approximation methods Mathematical model Moment methods Distributed parameter systems Testing
Content Type	Text
Resource Type	Article

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