in this paper, global robust stability of stochastic impulsive recurrent neural networks with time-varyingdelays which are represented by the takagi-sugeno (t-s) fuzzy models is considered. a novel linear matrix inequality (lmi)-based stability criterion is obtained by using lyapunov functional theory to guarantee the asymptotic stability of uncertain fuzzy stochastic impulsive recurrent neural...

In this paper, global robust stability of stochastic impulsive recurrent neural networks with time-varyingdelays which are represented by the Takagi-Sugeno (T-S) fuzzy models is considered. A novel Linear Matrix Inequality (LMI)-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of uncertain fuzzy stochastic impulsive recurrent neural...

Neuro-fuzzy systems-the combination of artiicial neural networks with fuzzy logic-are becoming increasingly popular. However, neuro-fuzzy systems need to be extended for applications which require context (e.g., speech, handwriting, control). Some of these applications can be modeled in the form of nite-state automata. Previously, it was proved that deterministic nite-state automata (DFAs) can ...

the main objective of this paper is to introduce a new intelligent optimization technique that uses a predictioncorrectionstrategy supported by a recurrent neural network for finding a near optimal solution of a givenobjective function. recently there have been attempts for using artificial neural networks (anns) in optimizationproblems and some types of anns such as hopfield network and boltzm...

the proposed iafc neural networks have both stability and plasticity because theyuse a control structure similar to that of the art-1(adaptive resonance theory) neural network.the unsupervised iafc neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. this fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. the supervised iafc ...

Computational reliability assessment in mechanized tunneling requires realistic numerical models accounting for the uncertainty of geotechnical and tunneling process parameters. For real-time prognoses, surrogate models are in general inevitable to substitute expensive complex numerical simulations. In this paper, different strategies for surrogate modeling in mechanized tunneling simulations a...

As a continuation of our study, this paper extends our research results of optimality-oriented stabilization from deterministic recurrent neural networks to stochastic recurrent neural networks, and presents a new approach to achieve optimally stochastic input-to-state stabilization in probability for stochastic recurrent neural networks driven by noise of unknown covariance. This approach is d...

In this paper, the problem of stability analysis for a class of impulsive stochastic fuzzy neural networks with timevarying delays and reaction-diffusion is considered. By utilizing suitable Lyapunov-Krasovskii funcational, the inequality technique and stochastic analysis technique, some sufficient conditions ensuring global exponential stability of equilibrium point for impulsive stochastic fu...

This paper deals with an impulsive stochastic fuzzy cellular neural networks with both time-varying and infinite distributed delays. Based on M-matrix theory and stochastic analysis technique, a sufficient condition is obtained to ensure the existence, uniqueness, and global exponential p−stability of the equilibrium point for the addressed impulsive stochastic fuzzy cellular neural network wit...