ℓ1-gain filter design of discrete-time positive neural networks with mixed delays. (February 2020)

• Record Type: Journal Article
• Authors: Xiao, Shunyuan; Zhang, Yijun; Zhang, Baoyong
• Is Part Of: Neural networks. Volume 122(2020)
• Page Start: 152
• Page End: 162
• Publication Date: 2020-02
• Subjects: Positive neural networks -- Mixed delays -- Filtering analysis -- ℓ1-gain performance; 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.2019.10.004
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: This paper mainly focuses on the filter design with ℓ 1 -gain disturbance attenuation performance for a class of discrete-time positive neural networks. Discrete and distributed time-varying delays occurring in neuron transmission are taken into account. Especially, the probabilistic distribution of distributed delays is described by a Bernoulli random process in the system model. First, criteria on the positiveness and the unique equilibrium of discrete-time neural networks are presented. Second, through linear Lyapunov method, sufficient conditions for globally asymptotic stability with ℓ 1 -gain disturbance attenuation performance of positive neural networks are proposed. Third, using the results obtained above, criteria on ℓ 1 -gain stability of the established filtering error system are presented, based on which a linear programming (LP) approach is put forward to design the desired positive filter. Finally, two examples of applications to water distribution network and genetic regulatory network are given to demonstrate the effectiveness and applicability of the derived results.