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Chin. Phys. B, 2020, Vol. 29(11): 110201    DOI: 10.1088/1674-1056/aba602
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Design of passive filters for time-delay neural networks with quantized output

Jing Han(韩静)1, Zhi Zhang(章枝)1, Xuefeng Zhang(张学锋)2, and Jianping Zhou(周建平)1,2, †
1 School of Computer Science & Technology, Anhui University of Technology, Ma’anshan 243032, China
2 Research Institute of Information Technology, Anhui University of Technology, Ma’anshan 243000, China
Abstract  

Passive filtering of neural networks with time-invariant delay and quantized output is considered. A criterion on the passivity of a filtering error system is proposed by means of the Lyapunov–Krasovskii functional and the Bessel–Legendre inequality. Based on the criterion, a design approach for desired passive filters is developed in terms of the feasible solution of a set of linear matrix inequalities. Then, analyses and syntheses are extended to the time-variant delay situation using the reciprocally convex combination inequality. Finally, a numerical example with simulations is used to illustrate the applicability and reduced conservatism of the present passive filter design approaches.

Keywords:  neural networks      time delay      quantization      filtering  
Received:  13 May 2020      Revised:  16 June 2020      Accepted manuscript online:  15 July 2020
Fund: the Excellent Youth Talent Support Program of Universities in Anhui Province, China (Grant No. GXYQZD2019021), the Major Research Project of Anhui Provincial Department of Education, China (Grant No. KJ2017ZD05), and the National Natural Science Foundation of China (Grant Nos. 61503002 and 61973199).
Corresponding Authors:  Corresponding author. E-mail: jpzhou0@gmail.com   

Cite this article: 

Jing Han(韩静), Zhi Zhang(章枝), Xuefeng Zhang(张学锋), and Jianping Zhou(周建平) Design of passive filters for time-delay neural networks with quantized output 2020 Chin. Phys. B 29 110201

Fig. 1.  

State trajectories of x1(t) and its estimation ${\mathop{x}\limits^{\unicode{x2323}}}_{1}(t)$.

Fig. 2.  

State trajectories of x2(t) and its estimation ${\mathop{x}\limits^{\unicode{x2323}}}_{2}(t)$.

Fig. 3.  

State trajectories of filtering error λ(t).

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