Einloggen

Thematische Bibliographien / Neurodynamic optimization / Zeitschriftenartikel

Um die anderen Arten von Veröffentlichungen zu diesem Thema anzuzeigen, folgen Sie diesem Link: Neurodynamic optimization.

Zeitschriftenartikel zum Thema „Neurodynamic optimization“

Autor: Grafiati

Veröffentlicht am 25. Mai 2024

Geben Sie eine Quelle nach APA, MLA, Chicago, Harvard und anderen Zitierweisen an

Wählen Sie eine Art der Quelle aus:

Machen Sie sich mit Top-50 Zeitschriftenartikel für die Forschung zum Thema "Neurodynamic optimization" bekannt.

Neben jedem Werk im Literaturverzeichnis ist die Option "Zur Bibliographie hinzufügen" verfügbar. Nutzen Sie sie, wird Ihre bibliographische Angabe des gewählten Werkes nach der nötigen Zitierweise (APA, MLA, Harvard, Chicago, Vancouver usw.) automatisch gestaltet.

Sie können auch den vollen Text der wissenschaftlichen Publikation im PDF-Format herunterladen und eine Online-Annotation der Arbeit lesen, wenn die relevanten Parameter in den Metadaten verfügbar sind.

Sehen Sie die Zeitschriftenartikel für verschiedene Spezialgebieten durch und erstellen Sie Ihre Bibliographie auf korrekte Weise.

1

Ji, Zheng, Xu Cai und Xuyang Lou. „A Quantum-Behaved Neurodynamic Approach for Nonconvex Optimization with Constraints“. Algorithms 12, Nr. 7 (05.07.2019): 138. http://dx.doi.org/10.3390/a12070138.

Der volle Inhalt der Quelle

Annotation:

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.

APA, Harvard, Vancouver, ISO und andere Zitierweisen

2

Le, Xinyi, Sijie Chen, Fei Li, Zheng Yan und Juntong Xi. „Distributed Neurodynamic Optimization for Energy Internet Management“. IEEE Transactions on Systems, Man, and Cybernetics: Systems 49, Nr. 8 (August 2019): 1624–33. http://dx.doi.org/10.1109/tsmc.2019.2898551.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

3

Li, Guocheng, und Zheng Yan. „Reconstruction of sparse signals via neurodynamic optimization“. International Journal of Machine Learning and Cybernetics 10, Nr. 1 (18.05.2017): 15–26. http://dx.doi.org/10.1007/s13042-017-0694-4.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

4

Leung, Man-Fai, und Jun Wang. „A Collaborative Neurodynamic Approach to Multiobjective Optimization“. IEEE Transactions on Neural Networks and Learning Systems 29, Nr. 11 (November 2018): 5738–48. http://dx.doi.org/10.1109/tnnls.2018.2806481.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

5

Ma, Litao, Jiqiang Chen, Sitian Qin, Lina Zhang und Feng Zhang. „An Efficient Neurodynamic Approach to Fuzzy Chance-constrained Programming“. International Journal on Artificial Intelligence Tools 30, Nr. 01 (29.01.2021): 2140001. http://dx.doi.org/10.1142/s0218213021400017.

Der volle Inhalt der Quelle

Annotation:

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.

APA, Harvard, Vancouver, ISO und andere Zitierweisen

6

Yan, Zheng, Jun Wang und Guocheng Li. „A collective neurodynamic optimization approach to bound-constrained nonconvex optimization“. Neural Networks 55 (Juli 2014): 20–29. http://dx.doi.org/10.1016/j.neunet.2014.03.006.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

7

Wang, Tong, Hao Cui, Zhongyi Zhang und Jian Wei. „A Neurodynamic Approach for SWIPT Power Splitting Optimization“. Journal of Physics: Conference Series 2517, Nr. 1 (01.06.2023): 012010. http://dx.doi.org/10.1088/1742-6596/2517/1/012010.

Der volle Inhalt der Quelle

Annotation:

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.

APA, Harvard, Vancouver, ISO und andere Zitierweisen

8

Liu, Bao, Xuehui Mei, Haijun Jiang und Lijun Wu. „A Nonpenalty Neurodynamic Model for Complex-Variable Optimization“. Discrete Dynamics in Nature and Society 2021 (16.02.2021): 1–10. http://dx.doi.org/10.1155/2021/6632257.

Der volle Inhalt der Quelle

Annotation:

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.

APA, Harvard, Vancouver, ISO und andere Zitierweisen

9

Zhao, You, Xiaofeng Liao und Xing He. „Novel projection neurodynamic approaches for constrained convex optimization“. Neural Networks 150 (Juni 2022): 336–49. http://dx.doi.org/10.1016/j.neunet.2022.03.011.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

10

Yan, Zheng, Jianchao Fan und Jun Wang. „A Collective Neurodynamic Approach to Constrained Global Optimization“. IEEE Transactions on Neural Networks and Learning Systems 28, Nr. 5 (Mai 2017): 1206–15. http://dx.doi.org/10.1109/tnnls.2016.2524619.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

11

Liu, Qingshan, Shaofu Yang und Jun Wang. „A Collective Neurodynamic Approach to Distributed Constrained Optimization“. IEEE Transactions on Neural Networks and Learning Systems 28, Nr. 8 (August 2017): 1747–58. http://dx.doi.org/10.1109/tnnls.2016.2549566.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

12

Qin, Sitian, Xinyi Le und Jun Wang. „A Neurodynamic Optimization Approach to Bilevel Quadratic Programming“. IEEE Transactions on Neural Networks and Learning Systems 28, Nr. 11 (November 2017): 2580–91. http://dx.doi.org/10.1109/tnnls.2016.2595489.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

13

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

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

14

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

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

15

Xu, Chen, Yiyuan Chai, Sitian Qin, Zhenkun Wang und 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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

16

Liu, Shuxin, Haijun Jiang, Liwei Zhang und 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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

17

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

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

18

Yan, Zheng, und Jun Wang. „Nonlinear Model Predictive Control Based on Collective Neurodynamic Optimization“. IEEE Transactions on Neural Networks and Learning Systems 26, Nr. 4 (April 2015): 840–50. http://dx.doi.org/10.1109/tnnls.2014.2387862.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

19

Fan, Jianchao, und Jun Wang. „A Collective Neurodynamic Optimization Approach to Nonnegative Matrix Factorization“. IEEE Transactions on Neural Networks and Learning Systems 28, Nr. 10 (Oktober 2017): 2344–56. http://dx.doi.org/10.1109/tnnls.2016.2582381.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

20

Yang, Shaofu, Qingshan Liu und Jun Wang. „A Collaborative Neurodynamic Approach to Multiple-Objective Distributed Optimization“. IEEE Transactions on Neural Networks and Learning Systems 29, Nr. 4 (April 2018): 981–92. http://dx.doi.org/10.1109/tnnls.2017.2652478.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

21

Che, Hangjun, und Jun Wang. „A Two-Timescale Duplex Neurodynamic Approach to Biconvex Optimization“. IEEE Transactions on Neural Networks and Learning Systems 30, Nr. 8 (August 2019): 2503–14. http://dx.doi.org/10.1109/tnnls.2018.2884788.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

22

Dai, Chengchen, Hangjun Che und Man-Fai Leung. „A Neurodynamic Optimization Approach for L1 Minimization with Application to Compressed Image Reconstruction“. International Journal on Artificial Intelligence Tools 30, Nr. 01 (29.01.2021): 2140007. http://dx.doi.org/10.1142/s0218213021400078.

Der volle Inhalt der Quelle

Annotation:

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.

APA, Harvard, Vancouver, ISO und andere Zitierweisen

23

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

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

24

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

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

25

Le, Xinyi, und Jun Wang. „A Two-Time-Scale Neurodynamic Approach to Constrained Minimax Optimization“. IEEE Transactions on Neural Networks and Learning Systems 28, Nr. 3 (März 2017): 620–29. http://dx.doi.org/10.1109/tnnls.2016.2538288.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

26

Wang, Jiasen, Jun Wang und Hangjun Che. „Task Assignment for Multivehicle Systems Based on Collaborative Neurodynamic Optimization“. IEEE Transactions on Neural Networks and Learning Systems 31, Nr. 4 (April 2020): 1145–54. http://dx.doi.org/10.1109/tnnls.2019.2918984.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

27

Che, Hangjun, und Jun Wang. „A Two-Timescale Duplex Neurodynamic Approach to Mixed-Integer Optimization“. IEEE Transactions on Neural Networks and Learning Systems 32, Nr. 1 (Januar 2021): 36–48. http://dx.doi.org/10.1109/tnnls.2020.2973760.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

28

Le, Xinyi, Sijie Chen, Zheng Yan und Juntong Xi. „A Neurodynamic Approach to Distributed Optimization With Globally Coupled Constraints“. IEEE Transactions on Cybernetics 48, Nr. 11 (November 2018): 3149–58. http://dx.doi.org/10.1109/tcyb.2017.2760908.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

29

Liu, Na, und Sitian Qin. „A Novel Neurodynamic Approach to Constrained Complex-Variable Pseudoconvex Optimization“. IEEE Transactions on Cybernetics 49, Nr. 11 (November 2019): 3946–56. http://dx.doi.org/10.1109/tcyb.2018.2855724.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

30

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

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

31

Drozdovski, A. K., A. A. Banayan und L. G. Ulyaeva. „Psycho-physiological approach to the problem of giftedness and high-quality sports selection“. Current Issues of Sports Psychology and Pedagogy 1, Nr. 1-2 (2021): 100–114. http://dx.doi.org/10.15826/spp.2021.1-2.11.

Der volle Inhalt der Quelle

Annotation:

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.

APA, Harvard, Vancouver, ISO und andere Zitierweisen

32

Demin, D. B., L. V. Poskotinova und Ye V. Krivonogova. „EEG CHARACTERISTICS AND THYROID PROFILE RATIO IN ADOLESCENTS OF SUBPOLAR AND POLAR EUROPEAN NORTH AREAS“. Bulletin of Siberian Medicine 12, Nr. 1 (28.02.2013): 24–29. http://dx.doi.org/10.20538/1682-0363-2013-1-24-29.

Der volle Inhalt der Quelle

Annotation:

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.

APA, Harvard, Vancouver, ISO und andere Zitierweisen

33

Wang, Yadi, Xiaoping Li und 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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

34

Chang, Xinyue, Yinliang Xu und 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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

35

Fang, Xiaomeng, Dong Pang, Juntong Xi und 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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

36

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

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

37

He, Shengzhan, Junjian Huang und Xing He. „Collective Neurodynamic Optimization for Image Segmentation by Binary Model with Constraints“. Cognitive Computation 12, Nr. 6 (27.10.2020): 1265–75. http://dx.doi.org/10.1007/s12559-020-09762-0.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

38

Zeng, Zhigang, Andrzej Cichocki, Long Cheng, Youshen Xia und Xiaolin Hu. „Guest Editorial Special Issue on Neurodynamic Systems for Optimization and Applications“. IEEE Transactions on Neural Networks and Learning Systems 27, Nr. 2 (Februar 2016): 210–13. http://dx.doi.org/10.1109/tnnls.2016.2515458.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

39

Nazemi, Alireza. „Solving general convex nonlinear optimization problems by an efficient neurodynamic model“. Engineering Applications of Artificial Intelligence 26, Nr. 2 (Februar 2013): 685–96. http://dx.doi.org/10.1016/j.engappai.2012.09.011.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

40

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

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

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, Nr. 2 (22.02.2012): 11–15. http://dx.doi.org/10.15690/vramn.v67i2.117.

Der volle Inhalt der Quelle

Annotation:

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.

APA, Harvard, Vancouver, ISO und andere Zitierweisen

42

Berga, David, und Xavier Otazu. „A Neurodynamic Model of Saliency Prediction in V1“. Neural Computation 34, Nr. 2 (14.01.2022): 378–414. http://dx.doi.org/10.1162/neco_a_01464.

Der volle Inhalt der Quelle

Annotation:

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.

APA, Harvard, Vancouver, ISO und andere Zitierweisen

43

Peng, Zhouhua, Jun Wang und Dan Wang. „Distributed Maneuvering of Autonomous Surface Vehicles Based on Neurodynamic Optimization and Fuzzy Approximation“. IEEE Transactions on Control Systems Technology 26, Nr. 3 (Mai 2018): 1083–90. http://dx.doi.org/10.1109/tcst.2017.2699167.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

44

Le, Xinyi, Zheng Yan und Juntong Xi. „A Collective Neurodynamic System for Distributed Optimization with Applications in Model Predictive Control“. IEEE Transactions on Emerging Topics in Computational Intelligence 1, Nr. 4 (August 2017): 305–14. http://dx.doi.org/10.1109/tetci.2017.2716377.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

45

Yan, Zheng, Xinyi Le und Jun Wang. „Tube-Based Robust Model Predictive Control of Nonlinear Systems via Collective Neurodynamic Optimization“. IEEE Transactions on Industrial Electronics 63, Nr. 7 (Juli 2016): 4377–86. http://dx.doi.org/10.1109/tie.2016.2544718.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

46

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

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

47

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

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

48

Li, Xinqi, Jun Wang und Sam Kwong. „A Discrete-Time Neurodynamic Approach to Sparsity-Constrained Nonnegative Matrix Factorization“. Neural Computation 32, Nr. 8 (August 2020): 1531–62. http://dx.doi.org/10.1162/neco_a_01294.

Der volle Inhalt der Quelle

Annotation:

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.

APA, Harvard, Vancouver, ISO und andere Zitierweisen

49

Wang, Tiancai, Xing He, Tingwen Huang, Chuandong Li und 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.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

50

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

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

Wir bieten Rabatte auf alle Premium-Pläne für Autoren, deren Werke in thematische Literatursammlungen aufgenommen wurden. Kontaktieren Sie uns, um einen einzigartigen Promo-Code zu erhalten!