NDLI: A neural network model for general minimax problem

Content Provider	IEEE Xplore Digital Library
Author	Zheng Yong-ling Ma Long-hua Qian Ji-xin
Copyright Year	2003
Description	Author affiliation: Dept. of Control Sci. & Eng., Zhejiang Univ., Hangzhou, China (Zheng Yong-ling; Ma Long-hua; Qian Ji-xin)
Abstract	Minimax problem is significant topic in signal process (e.g. filter design) and process control (e.g. controller design), which is relevant to robustness, parameters uncertainty, and signal noise etc. However, efficient algorithms are scarce, especially those for general minimax problem with equality and inequality nonlinear constraints. In this paper a novel neural network for general minimax problem has been constructed based on a penalty function approach. The unique request on objective function and constraint functions is that they are first-order differentiable. A Lyapunov function is established for the global stability analysis. The network is simulated and its validity is illustrated by numerical examples. Simulation results show that minimax neural network, which computes in second, is more efficient than the previous GA/SGA algorithms, which computes in minutes.
Sponsorship	Int. Neural Network Soc. IEEE Neural Networks Soc
Starting Page	879
Ending Page	884
File Size	360518
Page Count	6
File Format	PDF
ISBN	0780378989
ISSN	10987576
DOI	10.1109/IJCNN.2003.1223806
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-07-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Minimax techniques Signal processing Signal design Process control Signal processing algorithms Computational modeling Computer networks Filters Process design
Content Type	Text
Resource Type	Article

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