NDLI: RBF networks for nonlinear models subject to linear constraints

Content Provider	IEEE Xplore Digital Library
Author	Ya-Jun Qu Bao-Gang Hu
Copyright Year	2009
Description	Author affiliation: NLPR/LIAMA, Institute of Automation, Chinese Academy of Sciences, Beijing, China (Ya-Jun Qu; Bao-Gang Hu)
Abstract	In this work, we present a study of nonlinear modelings based on RBF networks. The incorporation of prior knowledge in modelings is our specific concern for adding transparency and improving the performance of the networks. We focus on the prior knowledge within the class of linear constraints, which includes both linear equality and linear inequality constraints. Different with other existing modeling approaches using Lagrange multiplier technique, we propose a sub-model using the same RBF network configuration to impose the constraints. Two benefits are gained from this modeling approach in comparison with the conventional RBF networks. First, the transparency is added through a structural way with a higher degree of explicitness than an algorithm means. Second, on linear equality constraint problems, the proposed approach is able to obtain the learning solutions directly without involving iteration processes. Numerical results from three benchmark examples confirm the beneficial aspects on the proposed modeling approach.
Starting Page	482
Ending Page	487
File Size	186798
Page Count	6
File Format	PDF
ISBN	9781424448302
DOI	10.1109/GRC.2009.5255075
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Automation Input variables Neural networks Radial basis function networks Feedforward neural networks Pattern recognition Function approximation Least squares approximation Lagrangian functions Least squares methods
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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