Exponential stability criterion of high-order BAM neural networks with delays and impulse via fixed point approach

2018 ◽  
Vol 292 ◽  
pp. 63-71 ◽  
Author(s):  
Zhilin Pu ◽  
Ruofeng Rao
Keyword(s):  
Neural Networks ◽  
Fixed Point ◽  
Exponential Stability ◽  
Stability Criterion ◽  
High Order ◽  
Bam Neural Networks ◽  
Fixed Point Approach

scholarly journals Fixed Points and Exponential Stability for Impulsive Time-Delays BAM Neural Networks via LMI Approach and Contraction Mapping Principle

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Ruofeng Rao ◽  
Zhilin Pu ◽  
Shouming Zhong ◽  
Xinggui Li
Keyword(s):  
Neural Networks ◽  
Fixed Point ◽  
Exponential Stability ◽  
Time Delays ◽  
Large Scale ◽  
Contraction Mapping ◽  
Contraction Mapping Principle ◽  
Bam Neural Networks ◽  
Lmi Approach ◽  
The Stability

The fixed point technique has been employed in the stability analysis of time-delays bidirectional associative memory (BAM) neural networks with impulse. By formulating a contraction mapping in a product space, a new LMI-based exponential stability criterion was derived. Lately, fixed point methods have educed various good results inspiring this work, but those criteria cannot be programmed by a computer. In this paper, LMI conditions of the obtained result can be applicable to computer Matlab LMI toolbox which meets the need of the large-scale calculation in real engineering. Moreover, a numerical example and a comparable table are presented to illustrate the effectiveness of the proposed methods.

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