Fault detection filtering of nonhomogeneous Markov switching memristive neural networks with output quantization

Abstract

This paper explores the fault detection filtering problem of Markov switching memristive neural networks with network-induced constraints in the discrete-time domain. The mode changes of memristive neural networks are described by a piecewise nonhomogeneous Markov process, whose transition probabilities are time-varying and governed by a higher-level nonhomogeneous Markov process. A generalized framework of Markov switching memristive neural networks includes the existing neural networks as special cases. In light of the limited communication bandwidth, the quantized measurement and packet dropouts are considered jointly. A mode-dependent fault detection filter is constructed to generate a residual signal and achieve better performance. From the mode-dependent yet time-varying Lyapunov functional, some less conservative sufficient conditions are devised for Markov switching memristive neural networks to ensure the performance level. Eventually, a simulation example is addressed to verify the feasibility of the attained theoretical analysis.