Consensus synchronization via quantized iterative learning for coupled fractional-order time-delayed competitive neural networks with input sharing.

Abstract

This paper presents the D^α-type distributed iterative learning control protocol to synchronize fractional-order competitive neural networks with time delay within a finite time frame. Firstly, the input sharing strategy of such desired competitive neural network is proposed by employing the average weighted combination of neural network, so that each neural network shares its input information to accelerate synchronization speed between competitive neural networks under a fixed communication topology. With the contraction mapping approach and bellman-gronwall inequality, the learning synchronization convergence of the distributed D^α-type iterative learning protocol is rigorously analyzed along the iterative axis. Subsequently, the communication topology between neural networks is extended to a iteration-varying topology with the number of iterations, and the learning sufficient conditions for network synchronization are provided. Finally, the efficiency of the designed D^α-type iterative learning synchronization methodology is validated through three numerical simulations.