\(p\)th moment exponential stability of stochastic memristor-based bidirectional associative memory (BAM) neural networks with time delays. (English) Zbl 1442.34132
Summary: Stochastic memristor-based bidirectional associative memory (BAM) neural networks with time delays play an increasingly important role in the design and implementation of neural network systems. Under the framework of Filippov solutions, the issues of the \(p\)th moment exponential stability of stochastic memristor-based BAM neural networks are investigated. By using the stochastic stability theory, Itô’s differential formula and Young inequality, the criteria are derived. Meanwhile, with Lyapunov approach and Cauchy-Schwarz inequality, we derive some sufficient conditions for the mean square exponential stability of the above systems. The obtained results improve and extend previous works on memristor-based or usual neural networks dynamical systems. Four numerical examples are provided to illustrate the effectiveness of the proposed results.
MSC:
| 34K60 | Qualitative investigation and simulation of models involving functional-differential equations |
| 34K50 | Stochastic functional-differential equations |
| 94C60 | Circuits in qualitative investigation and simulation of models |
| 92B20 | Neural networks for/in biological studies, artificial life and related topics |
| 34K20 | Stability theory of functional-differential equations |
| 34K39 | Discontinuous functional-differential equations |
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