Robust stability of fuzzy Markov type Cohen-Grossberg neural networks by delay decomposition approach

In this paper, we investigate the delay-dependent robust stability of fuzzy Cohen-Grossberg neural networks with Markovian jumping parameter and mixed time varying delays by delay decomposition method. A new Lyapunov-Krasovskii functional (LKF) is constructed by nonuniformly dividing discrete delay interval into multiple subinterval, and choosing proper functionals with different weighting matrices corresponding to different subintervals in the LKFs. A new delay-dependent stability condition is derived with Markovian jumping parameters by T-S fuzzy model. Based on the linear matrix inequality (LMI) technique, maximum admissible upper bound (MAUB) for the discrete and distributed delays are calculated by the LMI Toolbox in MATLAB. Numerical examples are given to illustrate the effectiveness of the proposed method.

Robust stability of fuzzy Markov type Cohen-Grossberg neural networks by delay decomposition approach Keywords:

Cohen-Grossberg neural networks , T-S fuzzy , Markovian jumping parameter , Linear matrix inequality , Lyapunov-Krasovskii functional , Maximum admissible upper bound