Novel discontinuous control for exponential synchronization of memristive recurrent neural networks with heterogeneous time-varying delays. Issue 5 (March 2018)

• Record Type: Journal Article
• Authors: Zhang, Ruimei; Zeng, Deqiang; Park, Ju H.; Zhong, Shouming; Yu, Yongbin
• Is Part Of: Journal of the Franklin Institute. Volume 355:Issue 5(2018)
• Page Start: 2826
• Page End: 2848
• Publication Date: 2018-03
• Subjects: Science -- Periodicals; Technology -- Periodicals; Patents -- United States -- Periodicals; 505
• DOI: 10.1016/j.jfranklin.2018.01.018
• Languages: English
• ISSNs: 0016-0032

Abstract

Abstract: This paper investigates the exponential synchronization problem of memristive recurrent neural networks (MRNNs) with heterogeneous time-varying delays (HTVDs). First, a novel discontinuous feedback control is designed, in which a tunable scalar is introduced. The tunable scalar makes the controller more flexible in reducing the upper bound of the control gain. Based on this control scheme, the double integral term can be successfully used to construct the LKF. Second, New method for tackling memristive synaptic weights and new estimation technique are presented. Third, based on the LKF and estimation technique, synchronization criterion is derived. In comparison with existing results, the established criterion is less conservatism thanks to the double integral term of the LKF. Finally, numerical simulations are presented to validate the effectiveness and advantages of the proposed results.