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Liao B, Han L, Cao X, Li S, Li J. Double integral‐enhanced Zeroing neural network with linear noise rejection for time‐varying matrix inverse. CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2023. [DOI: 10.1049/cit2.12161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/13/2023] Open
Affiliation(s)
- Bolin Liao
- College of Computer Science and Engineering Jishou University Jishou China
| | - Luyang Han
- College of Computer Science and Engineering Jishou University Jishou China
| | - Xinwei Cao
- School of Management Shanghai University Shanghai China
| | - Shuai Li
- School of Engineering Swansea University Swansea UK
| | - Jianfeng Li
- College of Computer Science and Engineering Jishou University Jishou China
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Prescribed-Time Convergent Adaptive ZNN for Time-Varying Matrix Inversion under Harmonic Noise. ELECTRONICS 2022. [DOI: 10.3390/electronics11101636] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Harmonic noises widely exist in industrial fields and always affect the computational accuracy of neural network models. The existing original adaptive zeroing neural network (OAZNN) model can effectively suppress harmonic noises. Nevertheless, the OAZNN model’s convergence rate only stays at the exponential convergence, that is, its convergence speed is usually greatly affected by the initial state. Consequently, to tackle the above issue, this work combines the dynamic characteristics of harmonic signals with prescribed-time convergence activation function, and proposes a prescribed-time convergent adaptive ZNN (PTCAZNN) for solving time-varying matrix inverse problem (TVMIP) under harmonic noises. Owing to the nonlinear activation function used having the ability to reject noises itself and the adaptive term also being able to compensate the influence of noises, the PTCAZNN model can realize double noise suppression. More importantly, the theoretical analysis of PTCAZNN model with prescribed-time convergence and robustness performance is provided. Finally, by varying a series of conditions such as the frequency of single harmonic noise, the frequency of multi-harmonic noise, and the initial value and the dimension of the matrix, the comparative simulation results further confirm the effectiveness and superiority of the PTCAZNN model.
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