An LMI-Based Approach to the Global Stability of Bidirectional Associative Memory Neural Networks with Variable Delay
Based on the linear matrix inequality (LMI), new sufficient conditions on the global exponential stability and asymptotic stability of bidirectional associative memory neural networks with variable delay are presented, and exponential converging velocity index is estimated. Furthermore, the results in this paper are less conservative than the ones reported so far in the literature. One example is given to illustrate the feasibility of our main results.
KeywordsNeural Network Asymptotic Stability Linear Matrix Inequality Global Stability Recurrent Neural Network
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