Abstract
This paper studies the non-fragile mixed H∞ and passive synchronization problem for Markov jump neural networks. The randomly occurring controller gain fluctuation phenomenon is investigated for non-fragile strategy. Moreover, the mixed time-varying delays composed of discrete and distributed delays are considered. By employing stochastic stability theory, synchronization criteria are developed for the Markov jump neural networks. On the basis of the derived criteria, the non-fragile synchronization controller is designed. Finally, an illustrative example is presented to demonstrate the validity of the control approach.
Highlights
There have been significant attentions on dynamic behaviors of neural networks, since they have various current and future potential applications, i.e., signal processing, optimization problems, pattern recognition and so forth. [1,2,3,4,5,6,7,8,9]
The study of Markov jump neural networks has been a significant topic during the past years, since this model can better describe the neural networks with different structures in real life
The mode jumps displayed in the Markov jump neural networks are commonly considered to be governed by an ideal homogeneous Markov chain
Summary
There have been significant attentions on dynamic behaviors of neural networks, since they have various current and future potential applications, i.e., signal processing, optimization problems, pattern recognition and so forth. [1,2,3,4,5,6,7,8,9]. There have been significant attentions on dynamic behaviors of neural networks, since they have various current and future potential applications, i.e., signal processing, optimization problems, pattern recognition and so forth. The synchronization problem has become a hot topic in the fields of neural networks [9, 12]. Time delays exist in neural networks, such that there is a need for the synchronization problem with time delays [20, 21]. It is noted that another unavoidable factor affecting the synchronization in neural networks is the disturbance. Several effective synchronization strategies for neural networks with disturbances have been proposed, especially for some finite-time cases [22,23,24,25,26,27,28]
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