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Mathematical Problems in Engineering
Volume 2017, Article ID 8314757, 16 pages
Research Article

Exponential Antisynchronization Control of Stochastic Memristive Neural Networks with Mixed Time-Varying Delays Based on Novel Delay-Dependent or Delay-Independent Adaptive Controller

1School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
2Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China
3Institute of Microstructure and Properties of Advanced Materials, Beijing University of Technology, Beijing 100124, China

Correspondence should be addressed to Weiping Wang; moc.621@888666ayihs and Xiong Luo; nc.ude.btsu@oulx

Received 2 December 2016; Accepted 6 February 2017; Published 27 March 2017

Academic Editor: Honglei Xu

Copyright © 2017 Minghui Yu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The global exponential antisynchronization in mean square of memristive neural networks with stochastic perturbation and mixed time-varying delays is studied in this paper. Then, two kinds of novel delay-dependent and delay-independent adaptive controllers are designed. With the ability of adapting to environment changes, the proposed controllers can modify their behaviors to achieve the best performance. In particular, on the basis of the differential inclusions theory, inequality theory, and stochastic analysis techniques, several sufficient conditions are obtained to guarantee the exponential antisynchronization between the drive system and response system. Furthermore, two numerical simulation examples are provided to the validity of the derived criteria.