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


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.

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The authors would like to thank the editors and anonymous reviewers for their valuable suggestions and constructive comments which have really helped the authors improve very much the presentation and quality of this paper.

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Correspondence to Dongsheng Guo.

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This work is supported by the National Natural Science Foundation of China with number 61603143, the Quanzhou City Science and Technology Program of China with number 2018C111R, the Promotion Program for Young and Middle-aged Teacher in Science and Technology Research of Huaqiao University with number ZQN-YX402, and the Scientific Research Funds of Huaqiao University with number 15BS410.

Appendix: Procedure of ZNN Design

Appendix: Procedure of ZNN Design

In this appendix, the general procedure of the ZNN design [13, 15,16,17,18,19] for time-varying problems solving is presented.

Specifically, as to a time-varying problem \(F(t)=0\in R^{m\times n}\) to be solved, the following matrix/vector-valued error function \(E(t)\in R^{m\times n}\) is firstly defined:

$$\begin{aligned} E(t):=F(t). \end{aligned}$$

Next, the time derivative \({\dot{E}}(t)\) of E(t) is selected such that E(t) can globally and exponentially converge to zero. More specifically, \({\dot{E}}(t)\) can be selected via the ZNN design formula [13, 15,16,17,18,19] as follows:

$$\begin{aligned} {\dot{E}}(t)=-\gamma {\mathcal {F}}(E(t)), \end{aligned}$$

where \(\gamma >0\in R\) denotes the parameter that is used to scale the convergence rate of the solution, and \({\mathcal {F}}(\cdot ): R^{m\times n}\rightarrow R^{m\times n}\) denotes the monotonically-increasing odd activation-function array. Finally, by expanding the ZNN design formula (11), the differential equation of a ZNN model is thus established for solving a specifical time-varying problem.

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Guo, D., Lin, X. Li-Function Activated Zhang Neural Network for Online Solution of Time-Varying Linear Matrix Inequality. Neural Process Lett (2020).

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  • Zhang neural network
  • Li activation function
  • Finite-time convergence
  • Theoretical results
  • Time-varying linear matrix inequality