Abstract
This paper discusses synchronization problem of two delayed stochastic neural networks. By constructing the Lyapunov functions, using stochastic analysis technique, some sufficient conditions guaranteeing the synchronization in mean square for the drive system with the response system have been derived in terms of linear matrix inequalities (LMIs) approach. Moreover, the main feature of this paper lies in that the present results are applicable for the drive system and the response system which all are considered not only in complex-valued domain but also with stochastic noise case. Two numerical examples are given to illustrate the effectiveness and merits of the present results.
Highlights
During the past several decades, neural network system has been a focal subject for research due to the widespread application in various of fields, such as pattern recognition, control theory and signal processing, associative memories and so on
The object of this paper is to study the synchronization between two delayed stochastic complex-valued neural networks(SCVNNs)
In the past studies, most of papers focused on the case that the drive system is deterministic while the response system is interfered with the outside stochastic noise, in this paper, we investigate synchronization for two delayed stochastic neural network systems
Summary
During the past several decades, neural network system has been a focal subject for research due to the widespread application in various of fields, such as pattern recognition, control theory and signal processing, associative memories and so on. In recent years, complex-valued neural networks(CVNNs) become an attractive research topic because of their potential and successful applications in computer vision, optoelectronics, quantum devices, remote sensing,and artificial neural information. Based on matrix measure method and matrix inequality theory, several criteria for the global exponentially synchronization of complex-valued neural networks are presented in [24]. M. Liu et al.: Global Synchronization of CVNNs With Stochastic Disturbances and Time-Varying Delay recurrent neural networks with time delays is studied. Due to the random disturbance caused by environmental uncertainties lead to the instability of dynamical neural network system, subsequently, stochastic synchronization problem for complex neural networks systems have attracted many research interests in nearest years. The object of this paper is to study the synchronization between two delayed stochastic complex-valued neural networks(SCVNNs). Represents the space of square-integrable vector functions over [0, +∞)
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