An overview of stability analysis and state estimation for memristive neural networks

This paper gives a review of recent advances on memristive neural networks with emphasis on the issues of stability analysis and state estimation. First, the concept of memristive neural network is recalled with a brief introduction of its background. Then, certain types of frequently seen neural networks are reviewed comprehensively with latest progress. Some engineering-oriented phenomena that appear extensively in the context of networked systems are introduced and summarized, including random dynamics, time-delays and network-induced incomplete information, etc. From different perspectives, several techniques explored for designing the required state estimators of memristive neural networks are discussed in detail. Some latest progress regarding the stability analysis and state estimation problems for discrete time memristive neural networks are presented. Finally, we provide the conclusions and point out certain future research directions. (C) 2020 Elsevier B.V. All rights reserved.