Quantized Control for Local Synchronization of Fractional-Order Neural Networks with Actuator Saturation

Abstract

This brief discusses the use of quantized control with actuator saturation to achieve the local synchronization of master–slave fractional-order neural networks (FONNs). A refined sector condition (RSC) is proposed that addresses the issue of the simultaneous quantizer effects and actuator constraints. The RSC is used in the theoretical analysis of local synchronization in drive-response systems. The analysis employs inequality techniques on the Mittag–Leffler function and fractional-order Lyapunov theory. Additionally, this paper presents two convex optimization algorithms that aim to minimize the actuator’s costs and expand the admissible initial area (AIA). Finally, this paper employs a three-neuron FONN to demonstrate the efficacy of the proposed methods.

Optimizing Controls to Track Moving Targets in an Intelligent Electro-Optical Detection System

“…However, it is difficult to adapt the traditional optical sighting of general-purpose ULWSs to this latest security concern [6]. Electro-optical detection systems (EODSs), which are intelligent precision targeting devices with specific functions of target imaging and tracking [7][8][9][10][11][12], are playing an important role in tracking moving targets.…”