Finite-time H∞ asynchronous state estimation for discrete-time fuzzy Markov jump neural networks with uncertain measurements

• Published in: Fuzzy sets and systems Vol. 356; pp. 113 - 128
• Main Authors: Shen, Hao, Xing, Mengping, Huo, Shicheng, Wu, Zheng-Guang, Park, Ju H.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2019
• Subjects: Asynchronous [formula omitted] state estimation; Finite-time stability; Fuzzy Markov jump neural networks; Uncertain measurements; Uncertain measurements; Finite-time stability; Asynchronous H∞ state estimation; Fuzzy Markov jump neural networks
• ISSN: 0165-0114; 1872-6801
• DOI: 10.1016/j.fss.2018.01.017

This paper is concerned with the problem of the H∞ asynchronous state estimation for fuzzy Markov jump neural networks (FMJNNs) with uncertain measurements over a finite-time interval. In terms of a Bernoulli distributed white sequence, the phenomenon of the randomly occurring uncertainties in the output equation is represented by exploiting a random variable with known occurrence probabilities. The main focus of this paper is to present a state estimator such that the resulting error system is finite-time bounded and satisfies an H∞ performance requirement. Then, by employing the stochastic analysis technique, sufficient conditions are provided to ensure that the state estimator is designed by means of solving a convex optimization problem. An example is finally given to explain the effectiveness and potentiality of the proposed design method.