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Author Hirasawa, K. ♦ Ohbayashi, M. ♦ Koga, M. ♦ Kusumi, N.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©1996
Language English
Subject Domain (in DDC) Technology ♦ Engineering & allied operations
Subject Keyword Asymptotic stability ♦ Nonlinear systems ♦ Nonlinear control systems ♦ Delay effects ♦ Large-scale systems ♦ Fluctuations ♦ Industrial plants ♦ Sufficient conditions ♦ Sampling methods ♦ Stability analysis
Abstract Higher order derivatives of the universal learning network (ULN) has been previously derived by forward and backward propagation computing methods, which can model and control the large scale complicated systems such as industrial plants, economic, social and life phenomena. In this paper, a new concept of nth order asymptotic orbital stability for the ULN is defined by using higher order derivatives of ULN and a sufficient condition of asymptotic orbital stability for ULN is derived. It is also shown that if 3rd order asymptotic orbital stability for a recurrent neural network is proved, higher order asymptotic orbital stability than 3rd order is guaranteed.
Description Author affiliation: Dept. of Electr. & Electron. Syst. Eng., Kyushu Univ., Fukuoka, Japan (Hirasawa, K.)
ISBN 0780332806
ISSN 1062922X
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1996-10-14
Publisher Place China
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 404.67 kB
Page Count 6
Starting Page 1352
Ending Page 1357


Source: IEEE Xplore Digital Library