NDLI: An embedded sigmoidal neural network for modeling of nonlinear systems

Content Provider	IEEE Xplore Digital Library
Author	Jinglu Hu Hirasawa, K.
Copyright Year	2001
Description	Author affiliation: Dept. of Electr. & Electron. Syst. Eng., Kyushu Univ., Fukuoka, Japan (Jinglu Hu)
Abstract	This paper discusses the problem of applying sigmoidal neural network to prediction and control of nonlinear dynamical systems. Instead of directly using neural networks as nonlinear models, we first develop a shield based on application specific knowledge, and then embed a sigmoidal neural network model in the shield. An embedded sigmoidal neural network model obtained in this way not only has a structure favorable for certain applications such as controller design, but also has useful interpretation on part of model parameters. Corresponding to the meaningful part and the meaningless part of model parameters, a hierarchical training algorithm consisting of two learning loops is introduced to train the model, which has good performance on solving local minimum problems. The usefulness of the proposed prediction model is demonstrated by applying it to prediction and control of a simulated nonlinear system.
Sponsorship	Int. Neural Network Soc.
Starting Page	1698
Ending Page	1703
File Size	489507
Page Count	6
File Format	PDF
ISBN	0780370449
ISSN	10987576
DOI	10.1109/IJCNN.2001.938417
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-07-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Nonlinear systems Nonlinear control systems Kernel Predictive models Control system synthesis MIMO Linearity Input variables Control design
Content Type	Text
Resource Type	Article

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