Finite-time filtering for T–S fuzzy jump neural networks with sector-bounded activation functions

• Published in: Neurocomputing (Amsterdam) Vol. 186; pp. 97 - 106
• Main Authors: Zhang, Yingqi, Mu, Jiankang, Shi, Yan, Zhang, Jianhua
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 19.04.2016
• Subjects: Criteria; Design parameters; Fuzzy; Fuzzy logic; Fuzzy neural networks; Markovian jump parameters; Mathematical analysis; Mathematical models; Neural networks; Parameters; Stochastic finite-time boundedness; Time-varying delays; Markovian jump parameters; Fuzzy neural networks; Stochastic finite-time boundedness; Time-varying delays
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2015.12.074

This paper is concerned with the problems of finite-time filter analysis and design for discrete time-delay neural networks with Markovian jump parameters and sector-bounded activation functions represented by Takagi–Sugeno fuzzy model. Firstly, a sufficient admissibility criterion is presented to guarantee that the augmented fuzzy jump neural network without parameter uncertainties is stochastically finite-time bounded in a prescribed time interval. From the admissibility criterion obtained, a sufficient condition on stochastic finite-time boundedness is provided for the augmented fuzzy jump neural networks. Then, a sufficient criterion to design a finite-time fuzzy filter is presented with uncertain parameters and Markovian jumps for discrete time-delay fuzzy neural networks. Moreover, conditions on stochastic finite-time stability are also established for nominal and uncertain delayed fuzzy jump neural networks without the presence of external disturbance. All criteria obtained can be represented as the form of linear matrix inequalities. Finally, numerical examples are given to illustrate the effectiveness of the obtained results.