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Tang Z, Mao M, Zhang Y, Tan N. Two Acceleration-Layer Configuration Amendment Schemes of Redundant Robot Arms Based on Zhang Neurodynamics Equivalency. Biomimetics (Basel) 2024; 9:435. [PMID: 39056876 PMCID: PMC11274851 DOI: 10.3390/biomimetics9070435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 07/10/2024] [Accepted: 07/11/2024] [Indexed: 07/28/2024] Open
Abstract
Two innovative acceleration-layer configuration amendment (CA) schemes are proposed to achieve the CA of constrained redundant robot arms. Specifically, by applying the Zhang neurodynamics equivalency (ZNE) method, an acceleration-layer CA performance indicator is derived theoretically. To obtain a unified-layer inequality constraint by transforming from angle-layer and velocity-layer constraints to acceleration-layer constraints, five theorems and three corollaries are theoretically derived and rigorously proved. Then, together with the unified acceleration-layer bound constraint, an enhanced acceleration-layer CA scheme specially considering three-layer time-variant physical limits is proposed, and a simplified acceleration-layer CA scheme considering three-layer time-invariant physical limits is also proposed. The proposed CA schemes are finally formulated in the form of standard quadratic programming and are solved by a projection neurodynamics solver. Moreover, comparative simulative experiments based on a four-link planar arm and a UR3 spatial arm are performed to verify the efficacy and superiority of the proposed CA schemes. At last, physical experiments are conducted on a real Kinova Jaco2 arm to substantiate the practicability of the proposed CA schemes.
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Affiliation(s)
- Zanyu Tang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China; (Z.T.); (Y.Z.); (N.T.)
- School of Computer Science and Engineering, Jishou University, Jishou 416000, China
| | - Mingzhi Mao
- School of Software Engineering, Sun Yat-sen University, Zhuhai 519082, China
| | - Yunong Zhang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China; (Z.T.); (Y.Z.); (N.T.)
| | - Ning Tan
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China; (Z.T.); (Y.Z.); (N.T.)
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2
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Xiao L, Cao P, Wang Z, Liu S. A novel fixed-time error-monitoring neural network for solving dynamic quaternion-valued Sylvester equations. Neural Netw 2024; 170:494-505. [PMID: 38039686 DOI: 10.1016/j.neunet.2023.11.058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 11/03/2023] [Accepted: 11/24/2023] [Indexed: 12/03/2023]
Abstract
This paper addresses the dynamic quaternion-valued Sylvester equation (DQSE) using the quaternion real representation and the neural network method. To transform the Sylvester equation in the quaternion field into an equivalent equation in the real field, three different real representation modes for the quaternion are adopted by considering the non-commutativity of quaternion multiplication. Based on the equivalent Sylvester equation in the real field, a novel recurrent neural network model with an integral design formula is proposed to solve the DQSE. The proposed model, referred to as the fixed-time error-monitoring neural network (FTEMNN), achieves fixed-time convergence through the action of a state-of-the-art nonlinear activation function. The fixed-time convergence of the FTEMNN model is theoretically analyzed. Two examples are presented to verify the performance of the FTEMNN model with a specific focus on fixed-time convergence. Furthermore, the chattering phenomenon of the FTEMNN model is discussed, and a saturation function scheme is designed. Finally, the practical value of the FTEMNN model is demonstrated through its application to image fusion denoising.
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Affiliation(s)
- Lin Xiao
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, Hunan 410081, China.
| | - Penglin Cao
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, Hunan 410081, China.
| | - Zidong Wang
- Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom.
| | - Sai Liu
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, Hunan 410081, China.
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3
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Wang D, Liu XW. A varying-parameter fixed-time gradient-based dynamic network for convex optimization. Neural Netw 2023; 167:798-809. [PMID: 37738715 DOI: 10.1016/j.neunet.2023.08.047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 07/05/2023] [Accepted: 08/28/2023] [Indexed: 09/24/2023]
Abstract
We focus on the fixed-time convergence and robustness of gradient-based dynamic networks for solving convex optimization. Most of the existing gradient-based dynamic networks with fixed-time convergence have limited ability to resist interferences of noises. To improve the convergence of the gradient-based dynamic networks, we design a new activation function and propose a gradient-based dynamic network with fixed-time convergence. The proposed dynamic network has a smaller upper bound of the convergence time than the existing dynamic networks with fixed-time convergence. A time-varying scaling parameter is employed to speed up the convergence. Our gradient-based dynamic network is proved to be robust against bounded noises and is able to resist the interference of unbounded noises. The numerical tests illustrate the effectiveness and superiority of the proposed network.
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Affiliation(s)
- Dan Wang
- School of Artificial Intelligence, Hebei University of Technology, Tianjin, 300401, China.
| | - Xin-Wei Liu
- Institute of Mathematics, Hebei University of Technology, Tianjin, 300401, China.
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4
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Wu W, Zhang Y. Novel adaptive zeroing neural dynamics schemes for temporally-varying linear equation handling applied to arm path following and target motion positioning. Neural Netw 2023; 165:435-450. [PMID: 37331233 DOI: 10.1016/j.neunet.2023.05.056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 04/19/2023] [Accepted: 05/29/2023] [Indexed: 06/20/2023]
Abstract
While the handling for temporally-varying linear equation (TVLE) has received extensive attention, most methods focused on trading off the conflict between computational precision and convergence rate. Different from previous studies, this paper proposes two complete adaptive zeroing neural dynamics (ZND) schemes, including a novel adaptive continuous ZND (ACZND) model, two general variable time discretization techniques, and two resultant adaptive discrete ZND (ADZND) algorithms, to essentially eliminate the conflict. Specifically, an error-related varying-parameter ACZND model with global and exponential convergence is first designed and proposed. To further adapt to the digital hardware, two novel variable time discretization techniques are proposed to discretize the ACZND model into two ADZND algorithms. The convergence properties with respect to the convergence rate and precision of ADZND algorithms are proved via rigorous mathematical analyses. By comparing with the traditional discrete ZND (TDZND) algorithms, the superiority of ADZND algorithms in convergence rate and computational precision is shown theoretically and experimentally. Finally, simulative experiments, including numerical experiments on a specific TVLE solving as well as four application experiments on arm path following and target motion positioning are successfully conducted to substantiate the efficacy, superiority, and practicability of ADZND algorithms.
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Affiliation(s)
- Wenqi Wu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China; Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Guangzhou 510006, China.
| | - Yunong Zhang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China; Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Guangzhou 510006, China.
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5
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Shi Y, Sheng W, Li S, Li B, Sun X, Gerontitis DK. A direct discretization recurrent neurodynamics method for time-variant nonlinear optimization with redundant robot manipulators. Neural Netw 2023; 164:428-438. [PMID: 37182345 DOI: 10.1016/j.neunet.2023.04.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 03/31/2023] [Accepted: 04/21/2023] [Indexed: 05/16/2023]
Abstract
Discrete time-variant nonlinear optimization (DTVNO) problems are commonly encountered in various scientific researches and engineering application fields. Nowadays, many discrete-time recurrent neurodynamics (DTRN) methods have been proposed for solving the DTVNO problems. However, these traditional DTRN methods currently employ an indirect technical route in which the discrete-time derivation process requires to interconvert with continuous-time derivation process. In order to break through this traditional research method, we develop a novel DTRN method based on the inspiring direct discrete technique for solving the DTVNO problem more concisely and efficiently. To be specific, firstly, considering that the DTVNO problem emerging in the discrete-time tracing control of robot manipulator, we further abstract and summarize the mathematical definition of DTVNO problem, and then we define the corresponding error function. Secondly, based on the second-order Taylor expansion, we can directly obtain the DTRN method for solving the DTVNO problem, which no longer requires the derivation process in the continuous-time environment. Whereafter, such a DTRN method is theoretically analyzed and its convergence is demonstrated. Furthermore, numerical experiments confirm the effectiveness and superiority of the DTRN method. In addition, the application experiments of the robot manipulators are presented to further demonstrate the superior performance of the DTRN method.
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Affiliation(s)
- Yang Shi
- School of Information Engineering, Yangzhou University, Yangzhou 225127, China; Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou 225127, China.
| | - Wangrong Sheng
- School of Information Engineering, Yangzhou University, Yangzhou 225127, China; Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou 225127, China
| | - Shuai Li
- College of Engineering, Swansea University, Fabian Way, Swansea, UK
| | - Bin Li
- School of Information Engineering, Yangzhou University, Yangzhou 225127, China; Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou 225127, China
| | - Xiaobing Sun
- School of Information Engineering, Yangzhou University, Yangzhou 225127, China; Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou 225127, China
| | - Dimitrios K Gerontitis
- Department of Information and Electronic Engineering International Hellenic University, Thessaloniki, Greece
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6
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An Anti-Interference Dynamic Integral Neural Network for Solving the Time-Varying Linear Matrix Equation with Periodic Noises. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2023.02.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2023]
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7
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Yang M, Zhang Y, Tan N, Hu H. Explicit Linear Left-and-Right 5-Step Formulas With Zeroing Neural Network for Time-Varying Applications. IEEE TRANSACTIONS ON CYBERNETICS 2023; 53:1133-1143. [PMID: 34464284 DOI: 10.1109/tcyb.2021.3104138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
In this article, being different from conventional time-discretization (simply called discretization) formulas, explicit linear left-and-right 5-step (ELLR5S) formulas with sixth-order precision are proposed. The general sixth-order ELLR5S formula with four variable parameters is developed first, and constraints of these four parameters are displayed to guarantee the zero stability, consistence, and convergence of the formula. Then, by choosing specific parameter values within constraints, eight specific sixth-order ELLR5S formulas are developed. The general sixth-order ELLR5S formula is further utilized to generate discrete zeroing neural network (DZNN) models for solving time-varying linear and nonlinear systems. For comparison, three conventional discretization formulas are also utilized. Theoretical analyses are presented to show the performance of ELLR5S formulas and DZNN models. Furthermore, abundant experiments, including three practical applications, that is, angle-of-arrival (AoA) localization and two redundant manipulators (PUMA560 manipulator and Kinova manipulator) control, are conducted. The synthesized results substantiate the efficacy and superiority of sixth-order ELLR5S formulas as well as the corresponding DZNN models.
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8
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Hua C, Cao X, Liao B, Li S. Advances on intelligent algorithms for scientific computing: an overview. Front Neurorobot 2023; 17:1190977. [PMID: 37152414 PMCID: PMC10161734 DOI: 10.3389/fnbot.2023.1190977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 04/06/2023] [Indexed: 05/09/2023] Open
Abstract
The field of computer science has undergone rapid expansion due to the increasing interest in improving system performance. This has resulted in the emergence of advanced techniques, such as neural networks, intelligent systems, optimization algorithms, and optimization strategies. These innovations have created novel opportunities and challenges in various domains. This paper presents a thorough examination of three intelligent methods: neural networks, intelligent systems, and optimization algorithms and strategies. It discusses the fundamental principles and techniques employed in these fields, as well as the recent advancements and future prospects. Additionally, this paper analyzes the advantages and limitations of these intelligent approaches. Ultimately, it serves as a comprehensive summary and overview of these critical and rapidly evolving fields, offering an informative guide for novices and researchers interested in these areas.
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Affiliation(s)
- Cheng Hua
- College of Computer Science and Engineering, Jishou University, Jishou, China
| | - Xinwei Cao
- School of Business, Jiangnan University, Wuxi, China
| | - Bolin Liao
- College of Computer Science and Engineering, Jishou University, Jishou, China
- *Correspondence: Bolin Liao
| | - Shuai Li
- Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
- VTT Technical Research Centre of Finland, Oulu, Finland
- Shuai Li
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9
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Jin J, Zhao L, Chen L, Chen W. A robust zeroing neural network and its applications to dynamic complex matrix equation solving and robotic manipulator trajectory tracking. Front Neurorobot 2022; 16:1065256. [PMID: 36457416 PMCID: PMC9705728 DOI: 10.3389/fnbot.2022.1065256] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 10/31/2022] [Indexed: 11/04/2023] Open
Abstract
Dynamic complex matrix equation (DCME) is frequently encountered in the fields of mathematics and industry, and numerous recurrent neural network (RNN) models have been reported to effectively find the solution of DCME in no noise environment. However, noises are unavoidable in reality, and dynamic systems must be affected by noises. Thus, the invention of anti-noise neural network models becomes increasingly important to address this issue. By introducing a new activation function (NAF), a robust zeroing neural network (RZNN) model for solving DCME in noisy-polluted environment is proposed and investigated in this paper. The robustness and convergence of the proposed RZNN model are proved by strict mathematical proof and verified by comparative numerical simulation results. Furthermore, the proposed RZNN model is applied to manipulator trajectory tracking control, and it completes the trajectory tracking task successfully, which further validates its practical applied prospects.
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Affiliation(s)
- Jie Jin
- School of Information Engineering, Changsha Medical University, Changsha, China
- School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, China
| | - Lv Zhao
- School of Information Engineering, Changsha Medical University, Changsha, China
- School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, China
| | - Lei Chen
- School of Information Engineering, Changsha Medical University, Changsha, China
- School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, China
| | - Weijie Chen
- School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, China
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10
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Jin J, Zhu J, Zhao L, Chen L. A fixed-time convergent and noise-tolerant zeroing neural network for online solution of time-varying matrix inversion. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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11
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Qiu B, Li XD, Yang S. A novel discrete-time neurodynamic algorithm for future constrained quadratic programming with wheeled mobile robot control. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07757-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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12
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Tan N, Li C, Yu P, Ni F. Two model-free schemes for solving kinematic tracking control of redundant manipulators using CMAC networks. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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13
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Hyperbolic Tangent Variant-Parameter Robust ZNN Schemes for Solving Time-Varying Control Equations and Tracking of Mobile Robot. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.08.066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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14
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Improved ZND model for solving dynamic linear complex matrix equation and its application. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07581-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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15
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Wang D, Liu XW. A gradient-type noise-tolerant finite-time neural network for convex optimization. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.01.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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16
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Recurrent neural networks as kinematics estimator and controller for redundant manipulators subject to physical constraints. Neural Netw 2022; 153:64-75. [DOI: 10.1016/j.neunet.2022.05.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 04/29/2022] [Accepted: 05/24/2022] [Indexed: 11/17/2022]
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17
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Huang S, Ma Z, Yu S, Han Y. New discrete-time zeroing neural network for solving time-variant underdetermined nonlinear systems under bound constraint. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2021.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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18
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Qiu B, Guo J, Li X, Zhang Z, Zhang Y. Discrete-Time Advanced Zeroing Neurodynamic Algorithm Applied to Future Equality-Constrained Nonlinear Optimization With Various Noises. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:3539-3552. [PMID: 32759087 DOI: 10.1109/tcyb.2020.3009110] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
This research first proposes the general expression of Zhang et al. discretization (ZeaD) formulas to provide an effective general framework for finding various ZeaD formulas by the idea of high-order derivative simultaneous elimination. Then, to solve the problem of future equality-constrained nonlinear optimization (ECNO) with various noises, a specific ZeaD formula originating from the general ZeaD formula is further studied for the discretization of a noise-perturbed continuous-time advanced zeroing neurodynamic model. Subsequently, the resulting noise-perturbed discrete-time advanced zeroing neurodynamic (NP-DTAZN) algorithm is proposed for the real-time solution to the future ECNO problem with various noises suppressed simultaneously. Moreover, theoretical and numerical results are presented to show the convergence and precision of the proposed NP-DTAZN algorithm in the perturbation of various noises. Finally, comparative numerical and physical experiments based on a Kinova JACO2 robot manipulator are conducted to further substantiate the efficacy, superiority, and practicability of the proposed NP-DTAZN algorithm for solving the future ECNO problem with various noises.
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Luo J, Yang H. A Robust Zeroing Neural Network Model Activated by the Special Nonlinear Function for Solving Time-Variant Linear System in Predefined-Time. Neural Process Lett 2022. [DOI: 10.1007/s11063-021-10726-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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20
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Shi Y, Jin L, Li S, Li J, Qiang J, Gerontitis DK. Novel Discrete-Time Recurrent Neural Networks Handling Discrete-Form Time-Variant Multi-Augmented Sylvester Matrix Problems and Manipulator Application. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:587-599. [PMID: 33074831 DOI: 10.1109/tnnls.2020.3028136] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In this article, the discrete-form time-variant multi-augmented Sylvester matrix problems, including discrete-form time-variant multi-augmented Sylvester matrix equation (MASME) and discrete-form time-variant multi-augmented Sylvester matrix inequality (MASMI), are formulated first. In order to solve the above-mentioned problems, in continuous time-variant environment, aided with the Kronecker product and vectorization techniques, the multi-augmented Sylvester matrix problems are transformed into simple linear matrix problems, which can be solved by using the proposed discrete-time recurrent neural network (RNN) models. Second, the theoretical analyses and comparisons on the computational performance of the recently developed discretization formulas are presented. Based on these theoretical results, a five-instant discretization formula with superior property is leveraged to establish the corresponding discrete-time RNN (DTRNN) models for solving the discrete-form time-variant MASME and discrete-form time-variant MASMI, respectively. Note that these DTRNN models are zero stable, consistent, and convergent with satisfied precision. Furthermore, illustrative numerical experiments are given to substantiate the excellent performance of the proposed DTRNN models for solving discrete-form time-variant multi-augmented Sylvester matrix problems. In addition, an application of robot manipulator further extends the theoretical research and physical realizability of RNN methods.
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21
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Li W, Han L, Xiao X, Liao B, Peng C. A gradient-based neural network accelerated for vision-based control of an RCM-constrained surgical endoscope robot. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06465-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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22
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Zeroing neural network for bound-constrained time-varying nonlinear equation solving and its application to mobile robot manipulators. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06068-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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23
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Xiao L, Dai J, Lu R, Li S, Li J, Wang S. Design and Comprehensive Analysis of a Noise-Tolerant ZNN Model With Limited-Time Convergence for Time-Dependent Nonlinear Minimization. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020; 31:5339-5348. [PMID: 32031952 DOI: 10.1109/tnnls.2020.2966294] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Zeroing neural network (ZNN) is a powerful tool to address the mathematical and optimization problems broadly arisen in the science and engineering areas. The convergence and robustness are always co-pursued in ZNN. However, there exists no related work on the ZNN for time-dependent nonlinear minimization that achieves simultaneously limited-time convergence and inherently noise suppression. In this article, for the purpose of satisfying such two requirements, a limited-time robust neural network (LTRNN) is devised and presented to solve time-dependent nonlinear minimization under various external disturbances. Different from the previous ZNN model for this problem either with limited-time convergence or with noise suppression, the proposed LTRNN model simultaneously possesses such two characteristics. Besides, rigorous theoretical analyses are given to prove the superior performance of the LTRNN model when adopted to solve time-dependent nonlinear minimization under external disturbances. Comparative results also substantiate the effectiveness and advantages of LTRNN via solving a time-dependent nonlinear minimization problem.
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