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Wang G, Hao Z, Li H, Zhang B. An activated variable parameter gradient‐based neural network for time‐variant constrained quadratic programming and its applications. CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2023. [DOI: 10.1049/cit2.12192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Affiliation(s)
- Guancheng Wang
- PAMI Research Group Department of Computer and Information Science University of Macau Taipa Macau
| | - Zhihao Hao
- PAMI Research Group Department of Computer and Information Science University of Macau Taipa Macau
- China Industrial Control Systems Cyber Emergency Response Team Beijing China
| | - Haisheng Li
- Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing China
| | - Bob Zhang
- PAMI Research Group Department of Computer and Information Science University of Macau Taipa Macau
- Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing China
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Double Accelerated Convergence ZNN with Noise-Suppression for Handling Dynamic Matrix Inversion. MATHEMATICS 2021. [DOI: 10.3390/math10010050] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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.
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