DocumentCode
428734
Title
A neural network model and its application
Author
Meiqin, Liu ; Senlin, Zhang ; Gangfeng, Yan ; Shouguang, Wang
Author_Institution
Coll. of Electr. Eng., Zhejiang Univ., Hangzhou, China
Volume
6
fYear
2004
fDate
10-13 Oct. 2004
Firstpage
5864
Abstract
A novel neural network model called standard neural network model (SNNM) was advanced, which was the interconnection of a linear dynamic system and a bounded static nonlinear operator. The SNNM could be represented by linear differential inclusion (LDI), which allowed us to take advantage of the linear matrix inequality (LMI) approach in the stability analysis or other performance analysis of the SNNM. By combining a number of different Lyapunov functions with S-procedure, some useful stability theorems for continuous time SNNM (CSNNM) and discrete time SNNM (DSNNM) were derived, whose conditions were formulated as LMIs. Some examples for the application of the SNNMs were presented, such as analyzing the stability of recurrent neural network (RNN), analyzing or synthesizing the neural network control system etc.
Keywords
Lyapunov methods; continuous time systems; discrete time systems; linear differential equations; linear matrix inequalities; linear systems; neurocontrollers; nonlinear control systems; recurrent neural nets; LMI; Lyapunov functions; bounded static nonlinear operator; linear differential inclusion; linear dynamic system interconnection; linear matrix inequality; neural network control system; recurrent neural network; stability theorems; standard neural network model; Asymptotic stability; Cellular neural networks; Control system synthesis; Control systems; Hopfield neural networks; Mathematical model; Neural networks; Performance analysis; Recurrent neural networks; Stability analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems, Man and Cybernetics, 2004 IEEE International Conference on
ISSN
1062-922X
Print_ISBN
0-7803-8566-7
Type
conf
DOI
10.1109/ICSMC.2004.1401131
Filename
1401131
Link To Document