Memory feedback finite-time control for memristive neutral-type neural networks with quantization

The drive-response of memory feedback control design for memristor neural networks of neutral type over finite-time domain is scrutinized in this paper. Notably, the main purpose of this work is to synthesize memory feedback controller in the presence of logarithmic quantizer and actuator saturation to guarantee the finite-time boundedness of the resulting memristive neural networks. On basis of proper Lyapunov?Krasovskii-functional and linear matrix inequalities, new sufficient criterian is established to assure the delay-dependent finite-time stabilization criteria for the addressed network model. Also, by solving the developed linear matrix inequalities, the finite-time memory feedback control law gain matrices could be attained. Eventually, the validations of the proposed mechanism are ultimately explored through two numerical examples.

Automated summary

This paper examines the memory feedback control design for memristor neural networks over a finite-time domain. By utilizing Lyapunov-Krasovskii-functional and linear matrix inequalities, new criteria are established to ensure delay-dependent finite-time stabilization. The proposed mechanism is validated through two numerical examples.