Mixed H∞ passivity based state estimation for fuzzy neural networks with Markovian-type estimator gain change

作者：Zhang, Dan; Cai, Wenjian^*; Wang, Qing-Guo

来源：Neurocomputing, 2014, 139: 321-327.

DOI：10.1016/j.neucom.2014.02.025

• State estimation
• Mixed H-infinity and passivity performance
• Markovian-type perturbation
• Time-varying delay
• Fuzzy neural networks

This paper is concerned with the mixed H-infinity and passivity based state estimation for a class of discrete-time fuzzy neural networks with the estimator gain change, where a discrete-time homogeneous Markov chain taking value in a finite set Gamma = {0,1} is introduced to model this phenomenon. Based on the Markovian system approach and linear matrix inequality technique, a new sufficient condition has been derived such that the estimation error system is exponentially stable in the mean square sense and achieves a prescribed mixed H-infinity and passivity performance level. The estimator parameter is then determined by solving a set of linear matrix inequalities (LMIs). A numerical example is presented to show the effectiveness of the proposed design method.

• 出版日期2014-9-2
• 单位浙江工业大学