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Global exponential stability for interval general bidirectional associative memory (BAM) neural networks with proportional delays
- Source :
- Mathematical Methods in the Applied Sciences. 39:5720-5731
- Publication Year :
- 2016
- Publisher :
- Wiley, 2016.
-
Abstract
- This paper is concerned with interval general bidirectional associative memory (BAM) neural networks with proportional delays. Using appropriate nonlinear variable transformations, the interval general BAM neural networks with proportional delays can be equivalently transformed into the interval general BAM neural networks with constant delays. The sufficient condition for the existence and uniqueness of equilibrium point of the model is established by applying Brouwer's fixed point theorem. By constructing suitable delay differential inequalities, some sufficient conditions for the global exponential stability of the model are obtained. Two examples are given to illustrate the effectiveness of the obtained results. This paper ends with a brief conclusion. Copyright © 2016 John Wiley & Sons, Ltd.
- Subjects :
- Equilibrium point
0209 industrial biotechnology
Artificial neural network
General Mathematics
General Engineering
Fixed-point theorem
02 engineering and technology
Interval (mathematics)
Nonlinear system
020901 industrial engineering & automation
Exponential stability
Control theory
0202 electrical engineering, electronic engineering, information engineering
Applied mathematics
020201 artificial intelligence & image processing
Bidirectional associative memory
Uniqueness
Mathematics
Subjects
Details
- ISSN :
- 01704214
- Volume :
- 39
- Database :
- OpenAIRE
- Journal :
- Mathematical Methods in the Applied Sciences
- Accession number :
- edsair.doi...........b9e04298c69dfb2b99c0d731f7e26c7a