Asymptotical state estimation of fuzzy cellular neural networks with time delay in the leakage term and mixed delays: Sample-data approach

Abstract

In this paper, the sampled measurement is used to estimate the neuron states, instead of the continuous measurement, and a sampled-data estimator is constructed. Leakage delay is used to unstable the neuron states. It is a challenging task to develop delay dependent condition to estimate the unstable neuron states through available sampled output measurements such that the error-state system is globally asymptotically stable. By constructing Lyapunov-Krasovskii functional (LKF), a sufficient condition depending on the sampling period is obtained in terms of linear matrix inequalities (LMIs). Moreover, by using the free-weighting matrices method, simple and efficient criterion is derived in terms of LMIs for estimation. 2010 MATHEMATICS SUBJECT CLASSIFICATION: 03B52; 92B20; 93C57; 93D20 ª 2015 Production and hosting by Elsevier B.V. on behalf of Egyptian Mathematical Society.

Adaptive Exponential Synchronization for Stochastic Competitive Neural Networks with Time-Varying Leakage Delays and Reaction-Diffusion Terms

“…In real world, the transmission delays often appear in leakage terms, which are called leakage delays [10]. It is well known that leakage delays have been incorporated into neural networks by many researchers [11][12][13][14]. However, leakage delays of neural networks in most bibliographies listed above are constants.…”

Dynamic Analysis of Fractional-Order Fuzzy BAM Neural Networks with Delays in the Leakage Terms

“…In [9], authors considered the following fractional-order autonomous neural network: Recently, a typical time delay called leakage delay which is the time delay in the leakage term of the systems and a considerable factor affecting dynamics for the worse in the systems, has a great impact on the dynamical behavior of neural networks. Since leakage delays can have a destabilizing influence on the dynamical behaviors of neural networks, it is necessary to investigate leakage delay effects on the stability of neural networks (see [12] [13] [14] [15] [16]). Fuzzy theory is considered as a more suitable method for the sake of taking vagueness into consideration; as a kind of important neural networks, studies have shown that the fuzzy neural networks are a very useful paradigm for image processing problems [17] [18].…”