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Journal articles on the topic 'Neurodynamic optimization'

Author: Grafiati

Published: 25 May 2024

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1

Ji, Zheng, Xu Cai, and Xuyang Lou. "A Quantum-Behaved Neurodynamic Approach for Nonconvex Optimization with Constraints." Algorithms 12, no. 7 (July 5, 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 evaluated through two function tests and three applications including the hollow transmission shaft, heat exchangers and crank–rocker mechanism. Numerical simulations are also provided to verify the advantages of our method.

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2

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 (August 2019): 1624–33. http://dx.doi.org/10.1109/tsmc.2019.2898551.

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3

Li, Guocheng, and Zheng Yan. "Reconstruction of sparse signals via neurodynamic optimization." International Journal of Machine Learning and Cybernetics 10, no. 1 (May 18, 2017): 15–26. http://dx.doi.org/10.1007/s13042-017-0694-4.

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4

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

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5

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 (January 29, 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 obtained. Thirdly, the boundedness, global existence and Lyapunov stability of the state solution to the proposed approach are proved. Moreover, the convergence to the optimal solution of considered problem is studied. Finally, several experiments are provided to show the performance of proposed approach.

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6

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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7

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 (June 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 is used to concurrently execute local search and update the initial state of the neural network by a particle swarm optimization method to get the global optimum. The experimental results demonstrate that the duplex neurodynamic-based SWIPT system maximizes information rate while satisfying the minimal harvesting energy requirement in a variety of channel states.

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8

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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9

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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10

Yan, Zheng, Jianchao Fan, and Jun Wang. "A Collective Neurodynamic Approach to Constrained Global Optimization." IEEE Transactions on Neural Networks and Learning Systems 28, no. 5 (May 2017): 1206–15. http://dx.doi.org/10.1109/tnnls.2016.2524619.

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11

Liu, Qingshan, Shaofu Yang, and Jun Wang. "A Collective Neurodynamic Approach to Distributed Constrained Optimization." IEEE Transactions on Neural Networks and Learning Systems 28, no. 8 (August 2017): 1747–58. http://dx.doi.org/10.1109/tnnls.2016.2549566.

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12

Qin, Sitian, Xinyi Le, and Jun Wang. "A Neurodynamic Optimization Approach to Bilevel Quadratic Programming." IEEE Transactions on Neural Networks and Learning Systems 28, no. 11 (November 2017): 2580–91. http://dx.doi.org/10.1109/tnnls.2016.2595489.

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13

Wu, Dawen, and Abdel Lisser. "Solving Constrained Pseudoconvex Optimization Problems with deep learning-based neurodynamic optimization." Mathematics and Computers in Simulation 219 (May 2024): 424–34. http://dx.doi.org/10.1016/j.matcom.2023.12.032.

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14

Leung, Man-Fai, and Jun Wang. "Cardinality-constrained portfolio selection based on collaborative neurodynamic optimization." Neural Networks 145 (January 2022): 68–79. http://dx.doi.org/10.1016/j.neunet.2021.10.007.

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15

Xu, Chen, Yiyuan Chai, Sitian Qin, Zhenkun Wang, and Jiqiang Feng. "A neurodynamic approach to nonsmooth constrained pseudoconvex optimization problem." Neural Networks 124 (April 2020): 180–92. http://dx.doi.org/10.1016/j.neunet.2019.12.015.

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16

Liu, Shuxin, Haijun Jiang, Liwei Zhang, and Xuehui Mei. "A neurodynamic optimization approach for complex-variables programming problem." Neural Networks 129 (September 2020): 280–87. http://dx.doi.org/10.1016/j.neunet.2020.06.012.

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17

Che, Hangjun, and Jun Wang. "A collaborative neurodynamic approach to global and combinatorial optimization." Neural Networks 114 (June 2019): 15–27. http://dx.doi.org/10.1016/j.neunet.2019.02.002.

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18

Yan, Zheng, and Jun Wang. "Nonlinear Model Predictive Control Based on Collective Neurodynamic Optimization." IEEE Transactions on Neural Networks and Learning Systems 26, no. 4 (April 2015): 840–50. http://dx.doi.org/10.1109/tnnls.2014.2387862.

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19

Fan, Jianchao, and Jun Wang. "A Collective Neurodynamic Optimization Approach to Nonnegative Matrix Factorization." IEEE Transactions on Neural Networks and Learning Systems 28, no. 10 (October 2017): 2344–56. http://dx.doi.org/10.1109/tnnls.2016.2582381.

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20

Yang, Shaofu, Qingshan Liu, and Jun Wang. "A Collaborative Neurodynamic Approach to Multiple-Objective Distributed Optimization." IEEE Transactions on Neural Networks and Learning Systems 29, no. 4 (April 2018): 981–92. http://dx.doi.org/10.1109/tnnls.2017.2652478.

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21

Che, Hangjun, and Jun Wang. "A Two-Timescale Duplex Neurodynamic Approach to Biconvex Optimization." IEEE Transactions on Neural Networks and Learning Systems 30, no. 8 (August 2019): 2503–14. http://dx.doi.org/10.1109/tnnls.2018.2884788.

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22

Dai, Chengchen, Hangjun Che, and Man-Fai Leung. "A Neurodynamic Optimization Approach for L1 Minimization with Application to Compressed Image Reconstruction." International Journal on Artificial Intelligence Tools 30, no. 01 (January 29, 2021): 2140007. http://dx.doi.org/10.1142/s0218213021400078.

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This paper presents a neurodynamic optimization approach for l1 minimization based on an augmented Lagrangian function. By using the threshold function in locally competitive algorithm (LCA), subgradient at a nondifferential point is equivalently replaced with the difference of the neuronal state and its mapping. The efficacy of the proposed approach is substantiated by reconstructing three compressed images.

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23

Jiang, Xinrui, Sitian Qin, Xiaoping Xue, and Xinzhi Liu. "A second-order accelerated neurodynamic approach for distributed convex optimization." Neural Networks 146 (February 2022): 161–73. http://dx.doi.org/10.1016/j.neunet.2021.11.013.

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24

Qin, Sitian, Yadong Liu, Xiaoping Xue, and Fuqiang Wang. "A neurodynamic approach to convex optimization problems with general constraint." Neural Networks 84 (December 2016): 113–24. http://dx.doi.org/10.1016/j.neunet.2016.08.014.

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25

Le, Xinyi, and Jun Wang. "A Two-Time-Scale Neurodynamic Approach to Constrained Minimax Optimization." IEEE Transactions on Neural Networks and Learning Systems 28, no. 3 (March 2017): 620–29. http://dx.doi.org/10.1109/tnnls.2016.2538288.

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26

Wang, Jiasen, Jun Wang, and Hangjun Che. "Task Assignment for Multivehicle Systems Based on Collaborative Neurodynamic Optimization." IEEE Transactions on Neural Networks and Learning Systems 31, no. 4 (April 2020): 1145–54. http://dx.doi.org/10.1109/tnnls.2019.2918984.

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27

Che, Hangjun, and Jun Wang. "A Two-Timescale Duplex Neurodynamic Approach to Mixed-Integer Optimization." IEEE Transactions on Neural Networks and Learning Systems 32, no. 1 (January 2021): 36–48. http://dx.doi.org/10.1109/tnnls.2020.2973760.

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28

Le, Xinyi, Sijie Chen, Zheng Yan, and Juntong Xi. "A Neurodynamic Approach to Distributed Optimization With Globally Coupled Constraints." IEEE Transactions on Cybernetics 48, no. 11 (November 2018): 3149–58. http://dx.doi.org/10.1109/tcyb.2017.2760908.

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29

Liu, Na, and Sitian Qin. "A Novel Neurodynamic Approach to Constrained Complex-Variable Pseudoconvex Optimization." IEEE Transactions on Cybernetics 49, no. 11 (November 2019): 3946–56. http://dx.doi.org/10.1109/tcyb.2018.2855724.

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30

Jia, Wenwen, Tingwen Huang, and Sitian Qin. "A collective neurodynamic penalty approach to nonconvex distributed constrained optimization." Neural Networks 171 (March 2024): 145–58. http://dx.doi.org/10.1016/j.neunet.2023.12.011.

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31

Drozdovski, A. K., A. A. Banayan, and L. G. Ulyaeva. "Psycho-physiological approach to the problem of giftedness and high-quality sports selection." Current Issues of Sports Psychology and Pedagogy 1, no. 1-2 (2021): 100–114. http://dx.doi.org/10.15826/spp.2021.1-2.11.

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The article notes that the problem of giftedness (talent) and highquality sports selection cannot be solved only by measuring anthropometric indicators, or only by tests-questionnaires, conversations, interviews, observations, which are dominate in the sports psychologists arsenal of nowadays. Meanwhile, the scientific developments of the national differential psychophysiology, that expand the possibilities for solving the problems indicated in the article, are ignored. The psychophysiological approach proposed by the authors is based on the method for assessing the natural predisposition of the subject to the definite sports specializations that presupposes the algorithm of actions: instrumental measurement of the nervous system’s properties (NSP, or otherwise, – neurodynamic characteristics) by E.P. Ilyin’s motor techniques; determination of the individual neurodynamic characteristics of the subject and their comparison with the known, experimentally identified “model” neurodynamic characteristics, which dominate, in terms of frequency of occurrence, among representatives of high-performance sports. The authors note a well-known scientific fact – the human’s NSP are rather conservative to changes in the growing-up process, which is essential to justification for the proposed psychophysiological approach to the problem of giftedness and selection in sports. The indications of scientific data are also significant, which are confirming the trend that with many possible combinations measuring by NSP, as a part of typological complexes (TC), the number of the latest is steeply reduced to several or even to one, dominating among athletes who have reached a high level of skill. The article states that the knowledge of the model neurodynamic characteristics dominating among representatives of different specializations in high-performance sports is the experimental basis, considering which it becomes possible early (6 years and older) identification of potentially gifted athletes, which is quite practicable if the neurodynamic characteristics of the subject for whom the choice of sports specialization is made are also known. The article notes that the optimization of training programs in the chosen sports specialization is impossible without knowing the severity of natural psychological abilities, peculiarities, and an example is given of such a forecast for athletes with different playing positions (forward, goaltender, defender), where the forecast is based on individual neurodynamic characteristics.

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32

Demin, D. B., L. V. Poskotinova, and Ye V. Krivonogova. "EEG CHARACTERISTICS AND THYROID PROFILE RATIO IN ADOLESCENTS OF SUBPOLAR AND POLAR EUROPEAN NORTH AREAS." Bulletin of Siberian Medicine 12, no. 1 (February 28, 2013): 24–29. http://dx.doi.org/10.20538/1682-0363-2013-1-24-29.

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Features of brain bioelectric activity and thyroid system in adolescents living in Subpolar andPolar regionsof the North are considered. Hyperactivity of subcortical diencephalic brain structures in adolescents of the Polar region is revealed. Adolescents of Subpolar region have more intensive age optimization of neurodynamic processes. There are noted latitude distinctions of thyroid hormones role for age formation of brain bioelectric activity in adolescents.

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33

Wang, Yadi, Xiaoping Li, and Jun Wang. "A neurodynamic optimization approach to supervised feature selection via fractional programming." Neural Networks 136 (April 2021): 194–206. http://dx.doi.org/10.1016/j.neunet.2021.01.004.

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34

Chang, Xinyue, Yinliang Xu, and Hongbin Sun. "Online distributed neurodynamic optimization for energy management of renewable energy grids." International Journal of Electrical Power & Energy Systems 130 (September 2021): 106996. http://dx.doi.org/10.1016/j.ijepes.2021.106996.

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35

Fang, Xiaomeng, Dong Pang, Juntong Xi, and Xinyi Le. "Distributed optimization for the multi-robot system using a neurodynamic approach." Neurocomputing 367 (November 2019): 103–13. http://dx.doi.org/10.1016/j.neucom.2019.08.032.

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36

Jiang, Xinrui, Sitian Qin, and Xiaoping Xue. "A penalty-like neurodynamic approach to constrained nonsmooth distributed convex optimization." Neurocomputing 377 (February 2020): 225–33. http://dx.doi.org/10.1016/j.neucom.2019.10.050.

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37

He, Shengzhan, Junjian Huang, and Xing He. "Collective Neurodynamic Optimization for Image Segmentation by Binary Model with Constraints." Cognitive Computation 12, no. 6 (October 27, 2020): 1265–75. http://dx.doi.org/10.1007/s12559-020-09762-0.

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38

Zeng, Zhigang, Andrzej Cichocki, Long Cheng, Youshen Xia, and Xiaolin Hu. "Guest Editorial Special Issue on Neurodynamic Systems for Optimization and Applications." IEEE Transactions on Neural Networks and Learning Systems 27, no. 2 (February 2016): 210–13. http://dx.doi.org/10.1109/tnnls.2016.2515458.

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39

Nazemi, Alireza. "Solving general convex nonlinear optimization problems by an efficient neurodynamic model." Engineering Applications of Artificial Intelligence 26, no. 2 (February 2013): 685–96. http://dx.doi.org/10.1016/j.engappai.2012.09.011.

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40

Huang, Banghua, Yang Liu, Yun-Liang Jiang, and Jun Wang. "Two-timescale projection neural networks in collaborative neurodynamic approaches to global optimization and distributed optimization." Neural Networks 169 (January 2024): 83–91. http://dx.doi.org/10.1016/j.neunet.2023.10.011.

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41

Demin, D. B. "THE ASSESSMENT OF REACTIONS OF POLYGRAPHIC PARAMETERS AT HRV-BIOFEEDBACK TRAINING IN ADOLESCENTS WITH DIFFERENT VARIANTS OF CARDIAC AUTONOMIC NERVOUS SYSTEM TONE." Annals of the Russian academy of medical sciences 67, no. 2 (February 22, 2012): 11–15. http://dx.doi.org/10.15690/vramn.v67i2.117.

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There is examine a character of change of brain bioelectric activity and polygraphic indicators at sessions of biofeedback by heart rhythm variability parameters (HRV-biofeedback) in 15–17 years adolescents who have different variants of cardiac autonomic nervous system tone. It is taped, that adolescents with cardiac balanced tone have more intensive optimization of functional brain activity in comparison with adolescents who have cardiac sympathetic tone — increase on alpha-activity and theta-activity depression in electroencephalogram structure. There were optimization of neurodynamic processes and most expressed stabilization of the hemodynamics indicators in adolescents with cardiac sympathetic tone after HRVbiofeedback training.

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42

Berga, David, and Xavier Otazu. "A Neurodynamic Model of Saliency Prediction in V1." Neural Computation 34, no. 2 (January 14, 2022): 378–414. http://dx.doi.org/10.1162/neco_a_01464.

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Abstract Lateral connections in the primary visual cortex (V1) have long been hypothesized to be responsible for several visual processing mechanisms such as brightness induction, chromatic induction, visual discomfort, and bottom-up visual attention (also named saliency). Many computational models have been developed to independently predict these and other visual processes, but no computational model has been able to reproduce all of them simultaneously. In this work, we show that a biologically plausible computational model of lateral interactions of V1 is able to simultaneously predict saliency and all the aforementioned visual processes. Our model's architecture (NSWAM) is based on Penacchio's neurodynamic model of lateral connections of V1. It is defined as a network of firing rate neurons, sensitive to visual features such as brightness, color, orientation, and scale. We tested NSWAM saliency predictions using images from several eye tracking data sets. We show that the accuracy of predictions obtained by our architecture, using shuffled metrics, is similar to other state-of-the-art computational methods, particularly with synthetic images (CAT2000-Pattern and SID4VAM) that mainly contain low-level features. Moreover, we outperform other biologically inspired saliency models that are specifically designed to exclusively reproduce saliency. We show that our biologically plausible model of lateral connections can simultaneously explain different visual processes present in V1 (without applying any type of training or optimization and keeping the same parameterization for all the visual processes). This can be useful for the definition of a unified architecture of the primary visual cortex.

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43

Peng, Zhouhua, Jun Wang, and Dan Wang. "Distributed Maneuvering of Autonomous Surface Vehicles Based on Neurodynamic Optimization and Fuzzy Approximation." IEEE Transactions on Control Systems Technology 26, no. 3 (May 2018): 1083–90. http://dx.doi.org/10.1109/tcst.2017.2699167.

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44

Le, Xinyi, Zheng Yan, and Juntong Xi. "A Collective Neurodynamic System for Distributed Optimization with Applications in Model Predictive Control." IEEE Transactions on Emerging Topics in Computational Intelligence 1, no. 4 (August 2017): 305–14. http://dx.doi.org/10.1109/tetci.2017.2716377.

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45

Yan, Zheng, Xinyi Le, and Jun Wang. "Tube-Based Robust Model Predictive Control of Nonlinear Systems via Collective Neurodynamic Optimization." IEEE Transactions on Industrial Electronics 63, no. 7 (July 2016): 4377–86. http://dx.doi.org/10.1109/tie.2016.2544718.

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46

Xiong, Wenxin, Christian Schindelhauer, Hing Cheung So, Joan Bordoy, Andrea Gabbrielli, and Junli Liang. "TDOA-based localization with NLOS mitigation via robust model transformation and neurodynamic optimization." Signal Processing 178 (January 2021): 107774. http://dx.doi.org/10.1016/j.sigpro.2020.107774.

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47

Yu, Yang, Wei Wang, and Kang-Hyun Jo. "Adaptive consensus control of output-constrained second-order nonlinear systems via neurodynamic optimization." Neurocomputing 295 (June 2018): 1–7. http://dx.doi.org/10.1016/j.neucom.2017.12.052.

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48

Li, Xinqi, Jun Wang, and Sam Kwong. "A Discrete-Time Neurodynamic Approach to Sparsity-Constrained Nonnegative Matrix Factorization." Neural Computation 32, no. 8 (August 2020): 1531–62. http://dx.doi.org/10.1162/neco_a_01294.

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Sparsity is a desirable property in many nonnegative matrix factorization (NMF) applications. Although some level of sparseness of NMF solutions can be achieved by using regularization, the resulting sparsity depends highly on the regularization parameter to be valued in an ad hoc way. In this letter we formulate sparse NMF as a mixed-integer optimization problem with sparsity as binary constraints. A discrete-time projection neural network is developed for solving the formulated problem. Sufficient conditions for its stability and convergence are analytically characterized by using Lyapunov's method. Experimental results on sparse feature extraction are discussed to substantiate the superiority of this approach to extracting highly sparse features.

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49

Wang, Tiancai, Xing He, Tingwen Huang, Chuandong Li, and Wei Zhang. "Collective neurodynamic optimization for economic emission dispatch problem considering valve point effect in microgrid." Neural Networks 93 (September 2017): 126–36. http://dx.doi.org/10.1016/j.neunet.2017.05.004.

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50

Liu, Na, and Sitian Qin. "A neurodynamic approach to nonlinear optimization problems with affine equality and convex inequality constraints." Neural Networks 109 (January 2019): 147–58. http://dx.doi.org/10.1016/j.neunet.2018.10.010.

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