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Robust partially mode‐dependent H ∞ filtering for discrete‐time nonhomogeneous Markovian jump neural networks with additive gain perturbations
Author(s) -
Zheng Dandan,
Hua Mingang,
Chen Junfeng,
Bian Cunkang,
Dai Weili
Publication year - 2019
Publication title -
mathematical methods in the applied sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.719
H-Index - 65
eISSN - 1099-1476
pISSN - 0170-4214
DOI - 10.1002/mma.5408
Subject(s) - mathematics , control theory (sociology) , polytope , lyapunov function , discrete time and continuous time , robustness (evolution) , jump , filter (signal processing) , artificial neural network , markov process , computer science , discrete mathematics , statistics , nonlinear system , biochemistry , physics , chemistry , control (management) , quantum mechanics , artificial intelligence , machine learning , computer vision , gene
This paper studies the robust partially mode‐dependent H ∞ filtering for nonhomogeneous Markovian jump neural networks with additive gain perturbations. The discrete time‐varying jump transition probability matrix is considered to be a polytope set. A partially mode‐dependent filter with additive gain perturbations is constructed to increase the robustness of the filter, which is subjects to H ∞ performance index. Based on the Lyapunov function approach, sufficient conditions are established such that the filtering error system is robustly stochastically stable. The efficiency of the new technique is illustrated by an illustrative example and a biological network example.