Dynamical behaviors of fuzzy reaction–diffusion periodic cellular neural networks with variable coefficients and delays

摘要: Abstract When modeling neural networks in a real world, not only diffusion effect and fuzziness cannot be avoided, but also self-inhibitions, interconnection weights, inputs should vary as time varies. In this paper, we discuss the dynamical behaviors of delayed reaction–diffusion fuzzy cellular with varying periodic weights well inputs. By using Halanay’s delay differential inequality, M -matrix theory analytic methods, some new sufficient conditions are obtained to ensure existence, uniqueness, global exponential stability solution, exponentially convergent rate index is estimated. particular, traditional assumption on differentiability time-varying delays no longer needed. The methodology developed paper shown simple effective for periodicity analysis delays. Two examples given show usefulness results that less restrictive than recently known criteria.