Synchronization of a novel model for memristive neural networks via sliding mode control. (November 2020)

• Record Type: Journal Article
• Authors: Li, Liangchen; Xu, Rui; Gan, Qintao; Lin, Jiazhe
• Is Part Of: ISA transactions. Volume 106(2020)
• Page Start: 31
• Page End: 39
• Publication Date: 2020-11
• Subjects: Memristive neural networks -- Synchronization -- Sliding mode control -- Time-varying delay; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2020.07.012
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: In this paper, a novel memristive neural networks model is developed. In the new model, the states of memristors are related to the initial resistance of the memristors and the amount of charge flowing through them in a specific direction, which embodies the memory characteristic of memristors. As a consequence, parameters in the model vary continuously and cannot be determined by the states of neurons. Existing results on synchronization of memristive neural networks are useless to this model. To investigate the synchronization of the new model, the main difficulty is how to deal with the time-varying parameter mismatches between the drive and response networks. Since the error is unbounded and only utilizing output feedback control is not enough, a sliding mode controller is designed. An integral sliding surface is designed for the desired sliding motion, and a feasible control law is proposed. Moreover, an example is given to demonstrate the novelty of our model and to illustrate the effectiveness of the sliding mode controller. Highlights: A novel model to describe memristive neural networks is developed. The new model embodies the memory characteristic of memristor. The synchronization of the new model is achieved by a suitable sliding model controller.