Journal
CHINESE JOURNAL OF PHYSICS
Volume 70, Issue -, Pages 271-287Publisher
ELSEVIER
DOI: 10.1016/j.cjph.2019.09.016
Keywords
Memristive neutral-type neural networks; Finite-time stabilization; Memory feedback control; Actuator saturation; Quantization
Categories
Ask authors/readers for more resources
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.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available