Mạng nơ-ron nhịp điệu biến thiên tham số mới để giải hệ phương trình ma trận theo thời gian trong môi trường nhiễu năng lượng hữu hạn và ứng dụng của nó vào cánh tay robot

Neural Computing and Applications - Tập 35 - Trang 22577-22593 - 2023
Chunquan Li1, Boyu Zheng1, Qingling Ou1, Qianqian Wang1, Chong Yue1, Limin Chen1, Zhijun Zhang2, Junzhi Yu3,4, Peter X. Liu5
1School of Information Engineering, Nanchang University, Nanchang, China
2School of Automation Science and Engineering, South China University of Technology, Guangzhou, China
3State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
4State Key Laboratory for Turbulence and Complex Systems, Department of Mechanics and Engineering Science, BIC-ESAT, College of Engineering, Peking University, Beijing, China
5Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada

Tóm tắt

Giải quyết phương trình ma trận với sự can thiệp của nhiễu là một vấn đề thách thức trong các ứng dụng toán học và kỹ thuật. Khác với mạng nơ-ron hồi tiếp truyền thống, một mạng nơ-ron nhịp điệu biến thiên tham số mới (VP-PRNN) được đề xuất và sử dụng để giải hệ phương trình ma trận theo thời gian trong môi trường nhiễu năng lượng hữu hạn trực tuyến. Đặc biệt, VP-PRNN có thể cho phép giải pháp trạng thái hội tụ đến giải pháp lý thuyết một cách nhanh chóng và mạnh mẽ, điều này cũng được chứng minh thông qua phân tích lý thuyết. Bốn loại nhiễu được sử dụng để thử nghiệm hệ thống, điều này chứng minh hiệu quả của VP-PRNN. So với mạng nơ-ron zeroing và mạng học nhịp sinh học với tham số cố định, VP-PRNN với tham số biến đổi cho thấy hiệu suất hội tụ vượt trội trong điều kiện có nhiễu năng lượng hữu hạn.

Từ khóa

#Mạng nơ-ron #hệ phương trình ma trận #môi trường nhiễu năng lượng hữu hạn #hội tụ #cánh tay robot

Tài liệu tham khảo

Brahma S, Datta B (2009) An optimization approach for minimum norm and robust partial quadratic eigenvalue assignment problems for vibrating structures. J Sound Vib 324(3–5):471–489 Calvetti D, Reichel L (1996) Application of adi iterative methods to the restoration of noisy images. SIAM J Matrix Anal Appl 17(1):165–186 Papyan V, Elad M (2015) Multi-scale patch-based image restoration. IEEE Trans Image Process 25(1):249–261 Knoll F, Holler M, Koesters T, Otazo R, Bredies K, Sodickson DK (2016) Joint mr-pet reconstruction using a multi-channel image regularizer. IEEE Trans Med Imag 36(1):1–16 Ding B, Wen G, Ma C, Yang X (2017) Evaluation of target segmentation on sar target recognition. In: 2017 4th International Conference on Information, Cybernetics and Computational Social Systems (ICCSS), pp. 663–667 . IEEE Chen X, Chen B, Guan J, Huang Y, He Y (2018) Space-range-doppler focus-based low-observable moving target detection using frequency diverse array mimo radar. IEEE Access 6:43892–43904 Ahmad AS (2017) Brain inspired cognitive artificial intelligence for knowledge extraction and intelligent instrumentation system. In: 2017 International Symposium on Electronics and Smart Devices (ISESD), pp. 352–356. IEEE Park J-H, Uhm T-Y, Bae G-D, Choi Y-H (2018) Stability evaluation of outdoor unmanned security robot in terrain information. In: 2018 18th International Conference on Control, Automation and Systems (ICCAS), pp. 955–957. IEEE Lee J-W, Park G-T, Shin J-S, Woo J-W (2017) Industrial robot calibration method using denavit-hatenberg parameters. In: 2017 17th International Conference on Control, Automation and Systems (ICCAS), pp. 1834–1837. IEEE Ma Z, Yu S, Han Y, Guo D (2021) Zeroing neural network for bound-constrained time-varying nonlinear equation solving and its application to mobile robot manipulators. Neural Comput Appl 33(21):14231–14245 Miao P, Wu D, Shen Y, Zhang Z (2019) Discrete-time neural network with two classes of bias noises for solving time-variant matrix inversion and application to robot tracking. Neural Comput Appl 31(9):4879–4890 Song Z, Lu Z, Wu J, Xiao X, Wang G (2022) Improved znd model for solving dynamic linear complex matrix equation and its application. Neural Comput Appl 34(23):21035–21048 Bartels RH, Stewart GW (1972) Solution of the matrix equation ax+ xb= c [f4]. Commun ACM 15(9):820–826 Ding F, Chen T (2005) Gradient based iterative algorithms for solving a class of matrix equations. IEEE Trans Automat Control 50(8):1216–1221 Zhang H, Yin H (2017) On the best convergence factors of iterative methods of matrix equations based on the gradient and least squares searches. In: 2017 36th Chinese Control Conference (CCC), pp. 111–115 . IEEE Li S, Chen S, Liu B (2013) Accelerating a recurrent neural network to finite-time convergence for solving time-varying sylvester equation by using a sign-bi-power activation function. Neural Process Lett 37:189–205 Mao M, Li J, Jin L, Li S, Zhang Y (2016) Enhanced discrete-time zhang neural network for time-variant matrix inversion in the presence of bias noises. Neurocomputing 207:220–230 Zuo Q, Li K, Xiao L, Wang Y, Li K (2021) On generalized zeroing neural network under discrete and distributed time delays and its application to dynamic lyapunov equation. IEEE Trans Syst Man Cybernet Syst 52(8):5114–5126 Madankan A (2010) Recurrent neural network for solving linear matrix equation. In: 2010 International Conference on Electronics and Information Engineering, vol. 2, pp. 2–70. IEEE Xiao L, Zhang Y, Dai J, Zuo Q, Wang S (2020) Comprehensive analysis of a new varying parameter zeroing neural network for time varying matrix inversion. IEEE Trans Ind Inform 17(3):1604–1613 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 98:102–113 Gong J, Jin J (2021) A better robustness and fast convergence zeroing neural network for solving dynamic nonlinear equations. Neural Comput Appl 35:77–87 Luo Y, Deng X, Wu J, Liu Y, Zhang Z (2021) A new finite-time circadian rhythms learning network for solving nonlinear and nonconvex optimization problems with periodic noises. IEEE Trans Cybernet 52(11):12514–12524 Jin L, Zhang Y, Li S (2015) Integration-enhanced zhang neural network for real-time-varying matrix inversion in the presence of various kinds of noises. IEEE Trans Neural Netw Learn Syst 27(12):2615–2627 Wang S, Dai S, Wang K (2015) Gradient-based neural network for online solution of lyapunov matrix equation with li activation function. In: 4th International Conference on Information Technology and Management Innovation, pp. 955–959. Atlantis Press Yi C, Chen Y, Lan X (2013) Comparison on neural solvers for the lyapunov matrix equation with stationary & nonstationary coefficients. Appl Math Model 37(4):2495–2502 Yan J, Jin L, Zhang R, Li H, Zhang J, Lu H (2019) Zeroing-type recurrent neural network for solving time-dependent lyapunov equation with noise rejection. In: 2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS), pp. 366–371 . IEEE Xiao L, Liao B, Luo J, Ding L (2017) A convergence-enhanced gradient neural network for solving sylvester equation. In: 2017 36th Chinese Control Conference (CCC), pp. 3910–3913. IEEE Wang J (1993) A recurrent neural network for real-time matrix inversion. Appl Math Comput 55(1):89–100 Xiao L, Zhang Y, Dai J, Li J, Li W (2019) New noise-tolerant znn models with predefined-time convergence for time-variant sylvester equation solving. IEEE Trans Syst Man Cybernet Syst 51(6):3629–3640 Zhang Y, Ge SS (2005) Design and analysis of a general recurrent neural network model for time-varying matrix inversion. IEEE Trans Neural Netw 16(6):1477–1490 Xiao L, Ding S, Mao M, Zhang Y, Liao B (2014) Finite-time convergence analysis and verification of improved znn for real-time matrix inversion. In: 2014 4th IEEE International Conference on Information Science and Technology, pp. 286–289. IEEE Zhang Z, Zheng L, Weng J, Mao Y, Lu W, Xiao L (2018) A new varying-parameter recurrent neural-network for online solution of time-varying sylvester equation. IEEE Trans Cybernet 48(11):3135–3148 Xiao L, He Y, Dai J, Liu X, Liao B, Tan H (2020) A variable-parameter noise-tolerant zeroing neural network for time-variant matrix inversion with guaranteed robustness. IEEE Trans Neural Netw Learn Syst 33(4):1535–1545 Xiao L, Li S, Lin F-J, Tan Z, Khan AH (2018) Zeroing neural dynamics for control design: comprehensive analysis on stability, robustness, and convergence speed. IEEE Trans Ind Inform 15(5):2605–2616 Xiao L, Song W, Li X, Jia L, Sun J, Wang Y (2021) Design and analysis of a noise-resistant znn model for settling time-variant linear matrix inequality in predefined-time. IEEE Trans Ind Inform 18(10):6840–6847 Zhang Z, Deng X, Kong L, Li S (2019) A circadian rhythms learning network for resisting cognitive periodic noises of time-varying dynamic system and applications to robots. IEEE Trans Cognit Dev Syst 12(3):575–587 Zhang Y, Li S, Kadry S, Liao B (2018) Recurrent neural network for kinematic control of redundant manipulators with periodic input disturbance and physical constraints. IEEE Trans Cybernet 49(12):4194–4205 Lokvenc J, Drtina R (2017) Asynchronous machine set for electrical laboratories part 1: Noise load. In: 2017 European Conference on Electrical Engineering and Computer Science (EECS), pp. 295–299. IEEE Sharma MK, Vig R (2014) Server noise: health hazard and its reduction using active noise control. In: 2014 Recent Advances in Engineering and Computational Sciences (RAECS), pp. 1–5. IEEE Lindner B, Garcıa-Ojalvo J, Neiman A, Schimansky-Geier L (2004) Effects of noise in excitable systems. Phys Rep 392(6):321–424 Zhu J, Liu X (2018) Measuring spike timing distance in the hindmarsh-rose neurons. Cognit Neurodyn 12:225–234 Zeng K, Huang J, Dong M (2014) White gaussian noise energy estimation and wavelet multi-threshold de-noising for heart sound signals. Circuits Syst Signal Process 33:2987–3002 Wen M, Basar E, Li Q, Zheng B, Zhang M (2017) Multiple-mode orthogonal frequency division multiplexing with index modulation. IEEE Trans Commun 65(9):3892–3906 Vieira TP, Tenório DF, da Costa JPC, de Freitas EP, Del Galdo G, de Sousa Júnior RT (2017) Model order selection and eigen similarity based framework for detection and identification of network attacks. J Netw Comput Appl 90:26–41 Lv J, Wang F (2015) Image laplace denoising based on sparse representation. In: 2015 International Conference on Computational Intelligence and Communication Networks (CICN), pp. 373–377. IEEE Wang Z, Chang J, Zhang S, Sun B, Jiang S, Luo S, Jia C, Liu Y, Liu X, Lv G et al (2014) An adaptive rayleigh noise elimination method in raman distributed temperature sensors using anti-stokes signal only. Opt Quant Electron 46:821–827 Sai Suneel A, Shiyamala S (2021) Peak detection based energy detection of a spectrum under rayleigh fading noise environment. J Ambient Intel Human Comput 12:4237–4245 Li S, Zhou M, Luo X (2017) Modified primal-dual neural networks for motion control of redundant manipulators with dynamic rejection of harmonic noises. IEEE Trans Neural Netw Learn Syst 29(10):4791–4801 Zhang Z, Ye L, Chen B, Luo Y (2023) An anti-interference dynamic integral neural network for solving the time-varying linear matrix equation with periodic noises. Neurocomputing 534:29–44 Shen L, Wu J, Yang S (2011) Initial position estimation in srm using bootstrap circuit without predefined inductance parameters. IEEE Trans Power Electron 26(9):2449–2456 Zhang Y, Ruan G, Li K, Yang Y (2010) Robustness analysis of the Zhang neural network for online time-varying quadratic optimization. J Phys A Math Theor 43(24):245202 Guo L, Li Y, Wang T, Wang H, Wang H (2017) Analysis and detection on noise characteristics of ac power transmission and transformation project in different voltage levels. In: 2017 2nd International Conference on Power and Renewable Energy (ICPRE), pp. 336–340. IEEE Girgis RS, Bernesjo M, Anger J (2009) Comprehensive analysis of load noise of power transformers. In: 2009 IEEE Power & Energy Society General Meeting, pp. 1–7. IEEE Huang Chuangxia, Liu Bingwen, Yang Hedi, Cao Jinde (2022) Positive almost periodicity on SICNNs incorporating mixed delays and D operator. Nonlinear Anal Model Control 27(4):719–739 Wang W (2022) Further results on mean-square exponential Input-to-State stability of stochastic delayed Cohen-Grossberg neural networks. Neural Processing Letters, 1–13