Asynchronous dissipative filtering for nonhomogeneous Markov switching neural networks with variable packet dropouts. (October 2020)

• Record Type: Journal Article
• Authors: Zhou, Xia; Cheng, Jun; Cao, Jinde; Ragulskis, Minvydas
• Is Part Of: Neural networks. Volume 130(2020)
• Page Start: 229
• Page End: 237
• Publication Date: 2020-10
• Subjects: Nonhomogeneous Markov switching -- Neural network -- Asynchronous filter -- Variable packet dropout; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2020.07.012
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: This work focuses on the problem of asynchronous filtering for nonhomogeneous Markov switching neural networks with variable packet dropouts (VPDs). The discrete-time nonhomogeneous Markov process is adopted to depict the modes switching of target plant, where time-varying transition probabilities are revealed by utilizing a polytope technology. By means of the Bernoulli distributed sequence, the randomly occurring packet dropouts are presented, where VPD rates are mode-dependent and remain variable. Unlike the existing results, the hidden Markov model scheme is formulated to describe the asynchronization between nonhomogeneous neural networks and filter, and resilient filters are presented, which makes the designed filters more general. Eventually, a simulation example is established to verify the effectiveness of the developed filter scheme.