Finite-time non-fragile control for synchronization of fractional-order stochastic neural networks

This study concerned with the synchronization problem over a finite-time domain for class of fractional-order stochastic neural networks via non-fragile controller discontinuous activation functions. Specifically, suggested state feedback sequentially cope scheme gain scheduling process. Notably, main objective this work is to obtained criterion resulting synchronized error system finite bound developed controller. For that, some consignment sufficient conditions derived systematically by implementing Lyapunov’s indirect method and stability theory which ensures stipulated networks. Later on, fluctuations that obeying certain white noise sequels from Bernoulli distribution are formulated in terms linear matrix inequalities. Eventually, illustrated substantiated through two numerical examples simulation results manifest advantage accuracy proposed criteria.