This article aims to study a class of discontinuous fuzzy inertial Cohen–Grossberg neural networks (DFICGNNs) with discrete and distributed time-delays. First all, in order deal the discontinuities by differential inclusion theory, based on generalized variable transformation including two tunable variables, mixed time-varying delayed DFICGNN is transformed into first-order system. Then, constr...

Stochastic effects on convergence dynamics of reaction-diffusion Cohen-Grossberg neural networks CGNNs with delays are studied. By utilizing Poincaré inequality, constructing suitable Lyapunov functionals, and employing the method of stochastic analysis and nonnegative semimartingale convergence theorem, some sufficient conditions ensuring almost sure exponential stability and mean square expon...

This work investigates a class of neural networks with cycle-symmetric connection strength. We shall show that, by changing the coordinates, the convergence of dynamics by Fiedler and Gedeon [Physica D 111 (1998) 288] is equivalent to the classical results. This presentation also addresses the extension of the convergence theorem to other classes of signal functions with saturations. In particu...

References 1 Norris, D. et al. (2000) Merging information in speech recognition: feedback is never necessary. Behav. Brain Sci. 23, 299–370 2 Norris, D. et al. (2003) Perceptual learning in speech. Cogn. Psychol. 47, 204–238 3 Fodor, J.A. (1983) Modularity of Mind: An Essay on Faculty Psychology, MIT Press 4 Grossberg, S. and Myers, C.W. (2000) The resonant dynamics of speech perception: interw...

Neural networks have been applied to various pattern classification and recognition problems for their learning ability, discrimination power, and generalization ability. The neural network most referenced in the pattern recognition literature are the multi-layer perceptron, the Kohonen associative memory and the Capenter-Grossberg ART network. The Kohonen memory runs an unsupervised clustering...

The dynamical behaviors of stochastic neural networks have appeared as a novel subject of research and applications, such as optimization, control, and image processing(see [1-12]). Obviously, finding stability criteria for these neural networks becomes an attractive research problem of importance. Some well results have just appeared, for example, in [1-5], for stochastic delayed Hopfield neur...