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Neural Processing Letters
Erschienen in: Neural Processing Letters 1/2020

18.06.2020

Li-Function Activated Zhang Neural Network for Online Solution of Time-Varying Linear Matrix Inequality

verfasst von: Dongsheng Guo, Xinjie Lin

Erschienen in: Neural Processing Letters | Ausgabe 1/2020

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Abstract

In the previous work, a typical recurrent neural network termed Zhang neural network (ZNN) has been developed for various time-varying problems solving. Based on the previous work, by exploiting a special activation function (i.e., Li activation function), the resultant ZNN model is presented and investigated in this paper for online solution of time-varying linear matrix inequality (TVLMI). For such a Li-function activated ZNN (LFAZNN) model, theoretical results are provided to show its excellent computational performance on solving the TVLMI. That is, the presented LFAZNN model has the property of finite-time convergence. Comparative simulation results with two illustrative examples further substantiate the efficacy of the presented LFAZNN model for TVLMI solving.
Vorheriger Artikel GrasNet: A Simple Grassmannian Network for Image Set Classification
Nächster Artikel A New Supervised Clustering Framework Using Multi Discriminative Parts and Expectation–Maximization Approach for a Fine-Grained Animal Breed Classification (SC-MPEM)

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Literatur
1.
Kim E, Kang HJ, Park M (1999) Numerical stability analysis of fuzzy control systems via quadratic programming and linear matrix inequalities. IEEE Trans Syst, Man, Cybern, Part A 29(4):333–346
2.
Chesi G, Garulli A, Tesi A, Vicino A (2003) Solving quadratic distance problems: An LMI-based approach. IEEE Trans Autom Control 48(2):200–212MathSciNetMATH
3.
Chesi G (2010) LMI techniques for optimization over polynomials in control: A survey. IEEE Trans Autom Control 55(11):2500–2510MathSciNetMATH
4.
Jing X (2012) Robust adaptive learning of feedforward neural networks via LMI optimizations. Neural Netw 31:33–45MATH
5.
Boyd S, Ghaoui LE, Feron E, Balakrishnan V (1994) Linear Matrix Inequalities in System and Control Theory. SIAM, PhiladelphiaMATH
6.
Liao X, Chen G, Sanchez EN (2002) Delay-dependent exponential stability analysis of delayed neural networks: An LMI approach. Neural Netw 15(7):855–866
7.
Lin C-L, Lai C-C, Huang T-H (2000) A neural network for linear matrix inequality problems. IEEE Trans Neural Netw 11(5):1078–1092
8.
Lin C-L, Huang T-H (2000) A novel approach solving for linear matrix inequalities using neural networks. Neural Process Lett 11(2):153–169MATH
9.
Cheng L, Hou ZG, Tan M (2009) A simplified neural network for linear matrix inequality problems. Neural Process Lett 29(3):213–230
10.
Su T-J, Huang M-Y, Hou C-L, Lin Y-J (2010) Cellular neural networks for gray image noise cancellation based on a hybrid linear matrix inequality and particle swarm optimization approach. Neural Process Lett 32(2):147–165
11.
Tan M (2016) Stabilization of coupled time-delay neural networks with nodes of different dimensions. Neural Process Lett 43(1):255–268
12.
Wu H, Zhang X, Xue S, Wang L, Wang Y (2016) LMI conditions to global Mittag-Leffler stability of fractional-order neural networks with impulses. Neurocomputing 193:148–154
13.
Guo D, Zhang Y (2014) Zhang neural network for online solution of time-varying linear matrix inequality aided with an equality conversion. IEEE Trans Neural Netw Learn Syst 25(2):370–382
14.
Hu X (2010) Dynamic system methods for solving mixed linear matrix inequalities and linear vector inequalities and equalities. Appl Math Comput 216(4):1181–1193MathSciNetMATH
15.
Zhang Y, Guo D (2015) Zhang Functions and Various Models. Springer-Verlag, HeidelbergMATH
16.
Zhang Y, Yi C (2011) Zhang Neural Networks and Neural-Dynamic Method. Nova Science Publishers, New York
17.
Guo D, Zhang Y (2012) A new variant of the Zhang neural network for solving online time-varying linear inequalities. Proc R Soc A. 468(2144):2255–2271MathSciNetMATH
18.
Zhang Y, Jiang D, Wang J (2002) A recurrent neural network for solving Sylvester equation with time-varying coefficients. IEEE Trans Neural Netw 13(5):1053–1063
19.
Sun J, Wang S, Wang K (2016) Zhang neural networks for a set of linear matrix inequalities with time-varying coefficient matrix. Inf Process Lett 116(10):603–610MATH
20.
Li S, Chen S, Liu B (2013) Accelerating a recurrent neural network to finite-time convergence for solving time-varying Sylvester equation by usinga sign-bi-power activation function. Neural Process Lett 37(2):189–205
21.
Zhang Y, Ding Y, Qiu B, Zhang Y, Li X (2017) Signum-function array activated ZNN with easier circuit implementation and finite-time convergence for linear systems solving. Inf Process Lett 124:30–34MathSciNetMATH
22.
Jin L, Li S, Wang H, Zhang Z (2018) Nonconvex projection activated zeroing neurodynamic models for time-varying matrix pseudoinversion with accelerated finite-time convergence. Appl Soft Comput 62:840–850
23.
Xiao L, Liao B, Li S, Zhang Z, Ding L, Jin L (2018) Design and analysis of FTZNN applied to the real-time solution of a nonstationary Lyapunov equation and tracking control of a wheeled mobile manipulator. IEEE Trans Ind Inform 14(1):98–105
24.
Xiao L, Liao B, Li S, Chen K (2018) Nonlinear recurrent neural networks for finite-time solution of general time-varying linear matrix equations. Neural Netw 98:102–113MATH
25.
Liu S, Trenkler G (2008) Hadamard, Khatri-Rao, Kronecker and other matrix products. Int J Coop Inf Syst 4(1):160–177MathSciNetMATH
26.
Mead C (1989) Analog VLSI and Neural Systems. Addison-Wesley, Boston, USAMATH
27.
Xiao L, Li S, Yang J, Zhang Z (2018) A new recurrent neural network with noise-tolerance and finite-time convergence for dynamic quadratic minimization. Neurocomputing 285:125–132
28.
Lv X, Xiao L, Tan Z (2019) Improved Zhang neural network with finite-time convergence for time-varying linear system of equations solving. Inf Process Lett 147:88–93MathSciNetMATH
Metadaten
Titel
Li-Function Activated Zhang Neural Network for Online Solution of Time-Varying Linear Matrix Inequality
verfasst von
Dongsheng Guo
Xinjie Lin
Publikationsdatum
18.06.2020
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2020
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-020-10291-y

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