Exponential Stabilization of Memristive Neural Networks With Time Delays

• Published in: IEEE transaction on neural networks and learning systems Vol. 23; no. 12; pp. 1919 - 1929
• Main Authors: Wu, Ailong, Zeng, Zhigang
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.12.2012; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Artificial intelligence; Biological neural networks; Cellular neural networks; Computer science; control theory; systems; Connectionism. Neural networks; Exact sciences and technology; Hybrid systems; Linear matrix inequalities; memristive neural networks; Memristors; Nanotechnology - methods; Neural Networks (Computer); Numerical models; optimal control; stabilization; Switches; Time Factors; Closed loop; Computer simulation; stabilization; Cellular neural nets; Control synthesis; Delay system; Neural network; Exponential stability; memristive neural networks; Optimization; Optimal design; Hybrid system; Neuromorphic system; Control loop; Optimal control; Linear matrix inequality; Hybrid systems; Memory circuit; Optimal control (mathematics)
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2012.2219554

In this paper, a general class of memristive neural networks with time delays is formulated and studied. Some sufficient conditions in terms of linear matrix inequalities are obtained, in order to achieve exponential stabilization. The result can be applied to the closed-loop control of memristive systems. In particular, several succinct criteria are given to ascertain the exponential stabilization of memristive cellular neural networks. In addition, a simplified and effective algorithm is considered for design of the optimal controller. These conditions are the improvement and extension of the existing results in the literature. Two numerical examples are given to illustrate the theoretical results via computer simulations.