Double Accelerated Convergence ZNN with Noise-Suppression for Handling Dynamic Matrix Inversion

Yongjun He, Bolin Liao, Lin Xiao, Luyang Han and Xiao Xiao
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Yongjun He: College of Information Science and Engineering, Jishou University, Jishou 416000, China
Bolin Liao: College of Information Science and Engineering, Jishou University, Jishou 416000, China
Lin Xiao: College of the Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha 410081, China
Luyang Han: College of Information Science and Engineering, Jishou University, Jishou 416000, China
Xiao Xiao: College of Information Science and Engineering, Jishou University, Jishou 416000, China

Mathematics, 2021, vol. 10, issue 1, 1-21

Abstract: Matrix inversion is commonly encountered in the field of mathematics. Therefore, many methods, including zeroing neural network (ZNN), are proposed to solve matrix inversion. Despite conventional fixed-parameter ZNN (FPZNN), which can successfully address the matrix inversion problem, it may focus on either convergence speed or robustness. So, to surmount this problem, a double accelerated convergence ZNN (DAZNN) with noise-suppression and arbitrary time convergence is proposed to settle the dynamic matrix inversion problem (DMIP). The double accelerated convergence of the DAZNN model is accomplished by specially designing exponential decay variable parameters and an exponential-type sign-bi-power activation function (AF). Additionally, two theory analyses verify the DAZNN model’s arbitrary time convergence and its robustness against additive bounded noise. A matrix inversion example is utilized to illustrate that the DAZNN model has better properties when it is devoted to handling DMIP, relative to conventional FPZNNs employing other six AFs. Lastly, a dynamic positioning example that employs the evolution formula of DAZNN model verifies its availability.

Keywords: zeroing neural network (ZNN); double accelerated convergence; dynamic matrix inversion problem; exponential decay variable parameters; noise-suppression; arbitrary time convergence; dynamic positioning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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