Reachable set estimation for inertial Markov jump BAM neural network with partially unknown transition rates and bounded disturbances. Issue 15 (October 2017)

• Record Type: Journal Article
• Authors: Ji, Huihui; Zhang, He; Senping, Tian
• Is Part Of: Journal of the Franklin Institute. Volume 354:Issue 15(2017)
• Page Start: 7158
• Page End: 7182
• Publication Date: 2017-10
• Subjects: Science -- Periodicals; Technology -- Periodicals; Patents -- United States -- Periodicals; 505
• DOI: 10.1016/j.jfranklin.2017.08.048
• Languages: English
• ISSNs: 0016-0032

Abstract

Abstract: This paper mainly focuses on the reachable set estimation problem of a time-varying delayed inertial Markov jump bidirectional associative memory (BAM) neural network with bounded disturbance inputs. The disturbances are assumed to be either unit-energy bounded or unit-peak bounded. Different from systems of the past studies, this paper is for inertial Markov jump BAM neural network with both time-varying delay and time-varying transition rates. The time-varying character of the considered transition rates is assumed to be piecewise-constant. In order to reduce the conservatism, the delay-partitioning technique is utilized to solve this reachable set estimation problem. As a result, it is obtained that the ellipsoid defined in this paper contains the reachable set R u p, which indicates the reachable set R u e is included. Further, we extend the results to the uncertain Markov jump BAM neutral network with partially unknown transition probabilities. Numerical examples are proposed to show the effectiveness of the given results.