Thematische Bibliographien / Neurodynamic optimization

Inhaltsverzeichnis

  1. Zeitschriftenartikel
  2. Dissertationen
  3. Buchteile
  4. Konferenzberichte

Auswahl der wissenschaftlichen Literatur zum Thema „Neurodynamic optimization“

Autor: Grafiati
Veröffentlicht am 25. Mai 2024
Zuletzt aktualisiert am 31. Juli 2025

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Zeitschriftenartikel zum Thema "Neurodynamic optimization"

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Ji, Zheng, Xu Cai, and Xuyang Lou. "A Quantum-Behaved Neurodynamic Approach for Nonconvex Optimization with Constraints." Algorithms 12, no. 7 (2019): 138. http://dx.doi.org/10.3390/a12070138.

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This paper presents a quantum-behaved neurodynamic swarm optimization approach to solve the nonconvex optimization problems with inequality constraints. Firstly, the general constrained optimization problem is addressed and a high-performance feedback neural network for solving convex nonlinear programming problems is introduced. The convergence of the proposed neural network is also proved. Then, combined with the quantum-behaved particle swarm method, a quantum-behaved neurodynamic swarm optimization (QNSO) approach is presented. Finally, the performance of the proposed QNSO algorithm is eva
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Le, Xinyi, Sijie Chen, Fei Li, Zheng Yan, and Juntong Xi. "Distributed Neurodynamic Optimization for Energy Internet Management." IEEE Transactions on Systems, Man, and Cybernetics: Systems 49, no. 8 (2019): 1624–33. http://dx.doi.org/10.1109/tsmc.2019.2898551.

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3

Ahmadi-Asl, Salman, Valentin Leplat, Anh Huy Phan, and Andrzej Cichocki. "Nonnegative Tensor Decomposition via Collaborative Neurodynamic Optimization." SIAM Journal on Scientific Computing 47, no. 1 (2025): C100—C125. https://doi.org/10.1137/23m1627304.

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Li, Guocheng, and Zheng Yan. "Reconstruction of sparse signals via neurodynamic optimization." International Journal of Machine Learning and Cybernetics 10, no. 1 (2017): 15–26. http://dx.doi.org/10.1007/s13042-017-0694-4.

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5

Leung, Man-Fai, and Jun Wang. "A Collaborative Neurodynamic Approach to Multiobjective Optimization." IEEE Transactions on Neural Networks and Learning Systems 29, no. 11 (2018): 5738–48. http://dx.doi.org/10.1109/tnnls.2018.2806481.

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Ma, Litao, Jiqiang Chen, Sitian Qin, Lina Zhang, and Feng Zhang. "An Efficient Neurodynamic Approach to Fuzzy Chance-constrained Programming." International Journal on Artificial Intelligence Tools 30, no. 01 (2021): 2140001. http://dx.doi.org/10.1142/s0218213021400017.

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In both practical applications and theoretical analysis, there are many fuzzy chance-constrained optimization problems. Currently, there is short of real-time algorithms for solving such problems. Therefore, in this paper, a continuous-time neurodynamic approach is proposed for solving a class of fuzzy chance-constrained optimization problems. Firstly, an equivalent deterministic problem with inequality constraint is discussed, and then a continuous-time neurodynamic approach is proposed. Secondly, a sufficient and necessary optimality condition of the considered optimization problem is obtain
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Yan, Zheng, Jun Wang, and Guocheng Li. "A collective neurodynamic optimization approach to bound-constrained nonconvex optimization." Neural Networks 55 (July 2014): 20–29. http://dx.doi.org/10.1016/j.neunet.2014.03.006.

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Wang, Tong, Hao Cui, Zhongyi Zhang, and Jian Wei. "A Neurodynamic Approach for SWIPT Power Splitting Optimization." Journal of Physics: Conference Series 2517, no. 1 (2023): 012010. http://dx.doi.org/10.1088/1742-6596/2517/1/012010.

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Abstract Simultaneous wireless information and power transfer (SWIPT) systems using energy from RF signals can effectively solve the energy shortage of wireless devices. However, the existing SWIPT optimization methods using numerical algorithms are difficult to solve the non-convex problem and to adapt to the dynamic communication circumstances. In this paper, a duplex neurodynamic optimization method is used to address the SWIPT system’s power partitioning issue. The information rate maximization problem of the SWIPT system is framed as a biconvex problem. A duplex recurrent neural network i
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Liu, Bao, Xuehui Mei, Haijun Jiang, and Lijun Wu. "A Nonpenalty Neurodynamic Model for Complex-Variable Optimization." Discrete Dynamics in Nature and Society 2021 (February 16, 2021): 1–10. http://dx.doi.org/10.1155/2021/6632257.

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In this paper, a complex-variable neural network model is obtained for solving complex-variable optimization problems described by differential inclusion. Based on the nonpenalty idea, the constructed algorithm does not need to design penalty parameters, that is, it is easier to be designed in practical applications. And some theorems for the convergence of the proposed model are given under suitable conditions. Finally, two numerical examples are shown to illustrate the correctness and effectiveness of the proposed optimization model.
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Zhao, You, Xiaofeng Liao, and Xing He. "Novel projection neurodynamic approaches for constrained convex optimization." Neural Networks 150 (June 2022): 336–49. http://dx.doi.org/10.1016/j.neunet.2022.03.011.

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Dissertationen zum Thema "Neurodynamic optimization"

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Tassouli, Siham. "Neurodynamic chance-constrained geometric optimization." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG062.

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Dans de nombreux problèmes réels, les décideurs sont confrontés à des incertitudes qui peuvent affecter les résultats de leurs décisions. Ces incertitudes découlent de diverses sources, telles que la variabilité de la demande, les conditions fluctuantes du marché ou des informations incomplètes sur les paramètres du système. Les approches traditionnelles d'optimisation déterministe supposent que tous les paramètres sont connus avec certitude, ce qui peut ne pas refléter avec précision la réalité du problème. L'optimisation sous contraintes de probabilité offre une approche plus réaliste et rob
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Wu, Dawen. "Solving Some Nonlinear Optimization Problems with Deep Learning." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG083.

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Cette thèse considère quatre types de problèmes d'optimisation non linéaire, à savoir les jeux de bimatrice, les équations de projection non linéaire (NPEs), les problèmes d'optimisation convexe non lisse (NCOPs) et les jeux à contraintes stochastiques (CCGs). Ces quatre classes de problèmes d'optimisation non linéaire trouvent de nombreuses applications dans divers domaines tels que l'ingénierie, l'informatique, l'économie et la finance. Notre objectif est d'introduire des algorithmes basés sur l'apprentissage profond pour calculer efficacement les solutions optimales de ces problèmes d'optim
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"A neurodynamic optimization approach to constrained pseudoconvex optimization." 2011. http://library.cuhk.edu.hk/record=b5894791.

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Guo, Zhishan.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.<br>Includes bibliographical references (p. 71-82).<br>Abstracts in English and Chinese.<br>Abstract --- p.i<br>Acknowledgement i --- p.ii<br>Chapter 1 --- Introduction --- p.1<br>Chapter 1.1 --- Constrained Pseudoconvex Optimization --- p.1<br>Chapter 1.2 --- Recurrent Neural Networks --- p.4<br>Chapter 1.3 --- Thesis Organization --- p.7<br>Chapter 2 --- Literature Review --- p.8<br>Chapter 2.1 --- Pseudo convex Optimization --- p.8<br>Chapter 2.2 --- Recurrent Neural Networks --- p.10<br>Chapter 3 --- Model De
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"Collective Neurodynamic Systems: Synchronization and Optimization." 2016. http://repository.lib.cuhk.edu.hk/en/item/cuhk-1292660.

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Buchteile zum Thema "Neurodynamic optimization"

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Jun, Wang. "Neurodynamic Optimization and Its Applications in Robotics." In Advances in Robotics. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03983-6_2.

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Leung, Man-Fai, and Jun Wang. "Neurodynamic Approaches to Cardinality-Constrained Portfolio Optimization." In Intelligent Systems Reference Library. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-61037-0_3.

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Qin, Sitian, Xinyi Le, and Jun Wang. "A Neurodynamic Optimization Approach to Bilevel Linear Programming." In Advances in Neural Networks – ISNN 2015. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25393-0_46.

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Leung, Man-Fai, and Jun Wang. "A Collaborative Neurodynamic Optimization Approach to Bicriteria Portfolio Selection." In Advances in Neural Networks – ISNN 2019. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22796-8_34.

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Fan, Jianchao, and Jun Wang. "A Collective Neurodynamic Optimization Approach to Nonnegative Tensor Decomposition." In Advances in Neural Networks - ISNN 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59081-3_25.

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Xu, Yiyao, and Sitian Qin. "A Penalty-Like Neurodynamic Approach to Convex Optimization Problems with Set Constraint." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4399-5_5.

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Liu, Qixing, Zhongying Chen, Yuhu Wu, and Tielong Shen. "A Collaborative Neurodynamic Optimization Algorithm of Eco-Routing with Electricity Allocation for PHEVs." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4399-5_3.

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Le, Xinyi, Sijie Chen, Yu Zheng, and Juntong Xi. "A Multiple-objective Neurodynamic Optimization to Electric Load Management Under Demand-Response Program." In Advances in Neural Networks - ISNN 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59081-3_21.

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Yan, Zheng, Jie Lu, and Guangquan Zhang. "Distributed Model Predictive Control of Linear Systems with Coupled Constraints Based on Collective Neurodynamic Optimization." In AI 2018: Advances in Artificial Intelligence. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03991-2_31.

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Wang, Jiasen, Jun Wang, and Dongbin Zhao. "Dynamically Weighted Model Predictive Control of Affine Nonlinear Systems Based on Two-Timescale Neurodynamic Optimization." In Advances in Neural Networks – ISNN 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64221-1_9.

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Konferenzberichte zum Thema "Neurodynamic optimization"

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Qi, Yucheng, Mengxin Wang, Xinrui Jiang, and Sitian Qin. "A Neurodynamic Approach for Distributed Optimization with Delayed Feedback." In 2025 13th International Conference on Intelligent Control and Information Processing (ICICIP). IEEE, 2025. https://doi.org/10.1109/icicip64458.2025.10898098.

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Huang, Haoen, Zhigang Zeng, and Jun Wang. "A Discrete-Time Collaborative Neurodynamic Approach to Distributed Global Optimization." In 2025 13th International Conference on Intelligent Control and Information Processing (ICICIP). IEEE, 2025. https://doi.org/10.1109/icicip64458.2025.10898108.

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Zhao, You, Xing He, Hangjun Che, and Huaqing Li. "Accelerated Projection Neurodynamic Approach for Convex Optimization with Affine Equality and Inequality Constraints." In 2024 International Conference on Neuromorphic Computing (ICNC). IEEE, 2024. https://doi.org/10.1109/icnc64304.2024.10987818.

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Zhang, Huiyan, Junrong Zhang, and Xin Han. "A Novel Accelerated Projection Neurodynamic Model for Convex Optimization Problem Constrained by Set and Linear-Equality." In 2024 6th International Conference on Electronic Engineering and Informatics (EEI). IEEE, 2024. http://dx.doi.org/10.1109/eei63073.2024.10696366.

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Li, Xinqi, Jun Wang, and Sam Kwong. "Alternative Mutation Operators in Collaborative Neurodynamic Optimization." In 2020 10th International Conference on Information Science and Technology (ICIST). IEEE, 2020. http://dx.doi.org/10.1109/icist49303.2020.9202136.

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Wang, Yadi, Xiaoping Li, and Jun Wang. "A Neurodynamic Approach to L0-Constrained Optimization." In 2020 12th International Conference on Advanced Computational Intelligence (ICACI). IEEE, 2020. http://dx.doi.org/10.1109/icaci49185.2020.9177499.

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Fang, Xiaomeng, Xinyi Le, and Fei Li. "Distributed Neurodynamic Optimization for Coordination of Redundant Robots." In 2019 9th International Conference on Information Science and Technology (ICIST). IEEE, 2019. http://dx.doi.org/10.1109/icist.2019.8836826.

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Wang, Jun. "Neurodynamic optimization and its applications for winners-take-all." In 2009 2nd IEEE International Conference on Computer Science and Information Technology. IEEE, 2009. http://dx.doi.org/10.1109/iccsit.2009.5235008.

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Fan, Jianchao, Ye Wang, Jianhua Zhao, Xiang Wang, and Xinxin Wang. "Blind source separation based on collective neurodynamic optimization approach." In 2017 36th Chinese Control Conference (CCC). IEEE, 2017. http://dx.doi.org/10.23919/chicc.2017.8027656.

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Wang, Jun. "Neurodynamic optimization with its application for model predictive control." In 2009 3rd International Workshop on Soft Computing Applications (SOFA). IEEE, 2009. http://dx.doi.org/10.1109/sofa.2009.5254883.

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