Добірка наукової літератури з теми "Weight adaptation algorithms"
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Статті в журналах з теми "Weight adaptation algorithms"
Zhong, Shuiming, Yu Xue, Yunhao Jiang, Yuanfeng Jin, Jing Yang, Ping Yang, Yuan Tian, and Mznah Al-Rodhaan. "A Sensitivity-Based Improving Learning Algorithm for Madaline Rule II." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/219679.
Повний текст джерелаMagoulas, G. D., M. N. Vrahatis, and G. S. Androulakis. "Improving the Convergence of the Backpropagation Algorithm Using Learning Rate Adaptation Methods." Neural Computation 11, no. 7 (October 1, 1999): 1769–96. http://dx.doi.org/10.1162/089976699300016223.
Повний текст джерелаYang, Feiran, Yin Cao, Ming Wu, Felix Albu, and Jun Yang. "Frequency-Domain Filtered-x LMS Algorithms for Active Noise Control: A Review and New Insights." Applied Sciences 8, no. 11 (November 20, 2018): 2313. http://dx.doi.org/10.3390/app8112313.
Повний текст джерелаCufoglu, Ayse, Mahi Lohi, and Colin Everiss. "Feature weighted clustering for user profiling." International Journal of Modeling, Simulation, and Scientific Computing 08, no. 04 (December 2017): 1750056. http://dx.doi.org/10.1142/s1793962317500568.
Повний текст джерелаWu, Di, Sheng Yao Yang, and J. C. Liu. "Cognitive Radio Decision Engine Based on Multi-Objective Genetic Algorithm." Applied Mechanics and Materials 48-49 (February 2011): 314–17. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.314.
Повний текст джерелаYiou, Pascal, and Aglaé Jézéquel. "Simulation of extreme heat waves with empirical importance sampling." Geoscientific Model Development 13, no. 2 (February 25, 2020): 763–81. http://dx.doi.org/10.5194/gmd-13-763-2020.
Повний текст джерелаKhabarlak, K. S. "FASTER OPTIMIZATION-BASED META-LEARNING ADAPTATION PHASE." Radio Electronics, Computer Science, Control, no. 1 (April 7, 2022): 82. http://dx.doi.org/10.15588/1607-3274-2022-1-10.
Повний текст джерелаLi, Miqing, and Xin Yao. "What Weights Work for You? Adapting Weights for Any Pareto Front Shape in Decomposition-Based Evolutionary Multiobjective Optimisation." Evolutionary Computation 28, no. 2 (June 2020): 227–53. http://dx.doi.org/10.1162/evco_a_00269.
Повний текст джерелаMiertoiu, Florin Ilarion, and Bogdan Dumitrescu. "Feasibility Pump Algorithm for Sparse Representation under Gaussian Noise." Algorithms 13, no. 4 (April 9, 2020): 88. http://dx.doi.org/10.3390/a13040088.
Повний текст джерелаSzuster, Marcin, and Piotr Gierlak. "Globalized Dual Heuristic Dynamic Programming in Control of Robotic Manipulator." Applied Mechanics and Materials 817 (January 2016): 150–61. http://dx.doi.org/10.4028/www.scientific.net/amm.817.150.
Повний текст джерелаДисертації з теми "Weight adaptation algorithms"
Haberstich, Cécile. "Adaptive approximation of high-dimensional functions with tree tensor networks for Uncertainty Quantification." Thesis, Ecole centrale de Nantes, 2020. http://www.theses.fr/2020ECDN0045.
Повний текст джерелаUncertainty quantification problems for numerical models require a lot of simulations, often very computationally costly (in time and/or memory). This is why it is essential to build surrogate models that are cheaper to evaluate. In practice, the output of a numerical model is represented by a function, then the objective is to construct an approximation.The aim of this thesis is to construct a controlled approximation of a function while using as few evaluations as possible.In a first time, we propose a new method based on weighted least-squares to construct the approximation of a function onto a linear approximation space. We prove that the projection verifies a numerical stability property almost surely and a quasi-optimality property in expectation. In practice we observe that the sample size is closer to the dimension of the approximation space than with existing weighted least-squares methods.For high-dimensional approximation, and in order to exploit potential low-rank structures of functions, we consider the model class of functions in tree-based tensor formats. These formats admit a multilinear parametrization with parameters forming a tree network of low-order tensors and are therefore also called tree tensor networks. In this thesis we propose an algorithm for approximating functions in tree-based tensor formats. It consists in constructing a hierarchy of nested subspaces associated to the different levels of the tree. The construction of these subspaces relies on principal component analysis extended to multivariate functions and the new weighted least-squares method. To reduce the number of evaluations necessary to build the approximation with a certain precision, we propose adaptive strategies for the control of the discretization error, the tree selection, the control of the ranks and the estimation of the principal components
(9828605), S. M. Rahman. "A feedforward neural network and its application to system indentification and control." Thesis, 1996. https://figshare.com/articles/thesis/A_feedforward_neural_network_and_its_application_to_system_indentification_and_control/20346819.
Повний текст джерелаThe aim of this thesis is to study a feedforward neural network and its application to system identification and control.
Attention is focused firstly on feedforward neural networks and their weight adaptation algorithms. A new class of weight adaptation learning algorithms are introduced based on the sliding mode concept. The effectiveness of the new class of algorithms are studied and simulations are conducted to present their performance.
Second part of this thesis deals with the application of the feedforward neural network with the developed learning algorithms. Two classes of problems are chosen to test the suitability of the feedforward neural network with proposed adaptation learning algorithms. The first problem is dynamic system identification and the other is dynamic system control. Results are presented in this thesis show the effectiveness of the feedforward neural network with the proposed learning algorithms in system identification and control.
Книги з теми "Weight adaptation algorithms"
Chistyakova, Guzel, Lyudmila Ustyantseva, Irina Remizova, Vladislav Ryumin, and Svetlana Bychkova. CHILDREN WITH EXTREMELY LOW BODY WEIGHT: CLINICAL CHARACTERISTICS, FUNCTIONAL STATE OF THE IMMUNE SYSTEM, PATHOGENETIC MECHANISMS OF THE FORMATION OF NEONATAL PATHOLOGY. au: AUS PUBLISHERS, 2022. http://dx.doi.org/10.26526/monography_62061e70cc4ed1.46611016.
Повний текст джерелаЧастини книг з теми "Weight adaptation algorithms"
Chistyakova, Guzel, Lyudmila Ustyantseva, Irina Remizova, Vladislav Ryumin, and Svetlana Bychkova. "CHARACTERISTICS OF CONNECTED AND ADAPTIVE IMMUNITY OF CHILDREN WITH EXTREMELY LOW BODY WEIGHT OF DIFFERENT GESTIONAL AGE." In CHILDREN WITH EXTREMELY LOW BODY WEIGHT: CLINICAL CHARACTERISTICS, FUNCTIONAL STATE OF THE IMMUNE SYSTEM, PATHOGENETIC MECHANISMS OF THE FORMATION OF NEONATAL PATHOLOGY, 47–77. au: AUS PUBLISHERS, 2022. http://dx.doi.org/10.26526/chapter_62061e70deca75.92242970.
Повний текст джерелаChistyakova, Guzel, Lyudmila Ustyantseva, Irina Remizova, Vladislav Ryumin, and Svetlana Bychkova. "FUNCTIONAL STATE OF THE IMMUNE SYSTEM OF CHILDREN WITH RETINOPATHY OF PREMATURE IN THE DYNAMICS OF THE POSTNATAL PERIOD." In CHILDREN WITH EXTREMELY LOW BODY WEIGHT: CLINICAL CHARACTERISTICS, FUNCTIONAL STATE OF THE IMMUNE SYSTEM, PATHOGENETIC MECHANISMS OF THE FORMATION OF NEONATAL PATHOLOGY, 105–28. au: AUS PUBLISHERS, 2022. http://dx.doi.org/10.26526/chapter_62061e70e0ba78.92986346.
Повний текст джерелаChistyakova, Guzel, Lyudmila Ustyantseva, Irina Remizova, Vladislav Ryumin, and Svetlana Bychkova. "FEATURES OF THE FUNCTIONAL STATE OF THE IMMUNE SYSTEM OF NEWBORNS WITH BRONCHOPULMONARY DYSPLASIA." In CHILDREN WITH EXTREMELY LOW BODY WEIGHT: CLINICAL CHARACTERISTICS, FUNCTIONAL STATE OF THE IMMUNE SYSTEM, PATHOGENETIC MECHANISMS OF THE FORMATION OF NEONATAL PATHOLOGY, 78–104. au: AUS PUBLISHERS, 2022. http://dx.doi.org/10.26526/chapter_62061e70dfbae2.28992721.
Повний текст джерелаChistyakova, Guzel, Lyudmila Ustyantseva, Irina Remizova, Vladislav Ryumin, and Svetlana Bychkova. "FEATURES OF THE POSTNATAL PERIOD OF PREMATURE INFANTS." In CHILDREN WITH EXTREMELY LOW BODY WEIGHT: CLINICAL CHARACTERISTICS, FUNCTIONAL STATE OF THE IMMUNE SYSTEM, PATHOGENETIC MECHANISMS OF THE FORMATION OF NEONATAL PATHOLOGY, 25–46. au: AUS PUBLISHERS, 2022. http://dx.doi.org/10.26526/chapter_62061e70ddd515.23232017.
Повний текст джерелаChistyakova, Guzel, Lyudmila Ustyantseva, Irina Remizova, Vladislav Ryumin, and Svetlana Bychkova. "RISK FACTORS OF BIRTH OF PREMATURE CHILDREN." In CHILDREN WITH EXTREMELY LOW BODY WEIGHT: CLINICAL CHARACTERISTICS, FUNCTIONAL STATE OF THE IMMUNE SYSTEM, PATHOGENETIC MECHANISMS OF THE FORMATION OF NEONATAL PATHOLOGY, 11–24. au: AUS PUBLISHERS, 2022. http://dx.doi.org/10.26526/chapter_62061e70dcd948.10387409.
Повний текст джерелаSerra, Ginalber Luiz de Oliveira, and Edson B. M. Costa. "Robust Stability Self-Tuning Fuzzy PID Digital Controller." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 141–54. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3129-6.ch006.
Повний текст джерелаAbdelbar, Ashraf M., Islam Elnabarawy, Donald C. Wunsch II, and Khalid M. Salama. "Ant Colony Optimization Applied to the Training of a High Order Neural Network with Adaptable Exponential Weights." In Advances in Computational Intelligence and Robotics, 362–74. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0063-6.ch014.
Повний текст джерелаAbdelbar, Ashraf M., Islam Elnabarawy, Donald C. Wunsch II, and Khalid M. Salama. "Ant Colony Optimization Applied to the Training of a High Order Neural Network with Adaptable Exponential Weights." In Deep Learning and Neural Networks, 82–95. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0414-7.ch006.
Повний текст джерелаGoldberg, David E. "John H. Holland, Facetwise Models, and Economy of Thought." In Perspectives on Adaptation in Natural and Artificial Systems. Oxford University Press, 2005. http://dx.doi.org/10.1093/oso/9780195162929.003.0008.
Повний текст джерелаТези доповідей конференцій з теми "Weight adaptation algorithms"
Zein-Sabatto, Saleh, Alireza Behbahani, Richard Hans Mgaya, and Mohammad Bodruzzaman. "Turbine Engine Reconfigurable Control Systems for Aircraft Propulsion Performance Improvement." In ASME Turbo Expo 2013: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/gt2013-94910.
Повний текст джерелаSanjuan, Marco E. "Neural-Network Based On-Line Adaptation of Model Predictive Controller for Dynamic Systems With Uncertain Behavior." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-82609.
Повний текст джерелаWang, Cheng, Chang-qi Yan, Jian-jun Wang, Lei Chen, and Gui-jing Li. "Application of Dual-Adaptive Niched Genetic Algorithm in Optimal Design of Nuclear Power Components." In 2014 22nd International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/icone22-30120.
Повний текст джерелаDominique, S., and J. Y. Tre´panier. "Automated Preliminary Structural Rotor Design Using Genetic Algorithms and Neural Networks." In ASME Turbo Expo 2008: Power for Land, Sea, and Air. ASMEDC, 2008. http://dx.doi.org/10.1115/gt2008-51181.
Повний текст джерелаZuo, Lei, and Samir A. Nayfeh. "Adaptive Least-Mean Square Feed-Forward Control With Actuator Saturation by Direct Minimization." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-85494.
Повний текст джерелаSteffens Henrique, Alisson, Vinicius Almeida dos Santos, and Rodrigo Lyra. "NEAT Snake: a both evolutionary and neural network adaptation approach." In Computer on the Beach. Itajaí: Universidade do Vale do Itajaí, 2020. http://dx.doi.org/10.14210/cotb.v11n1.p052-053.
Повний текст джерелаBouvier, Victor, Philippe Very, Clément Chastagnol, Myriam Tami, and Céline Hudelot. "Robust Domain Adaptation: Representations, Weights and Inductive Bias (Extended Abstract)." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/644.
Повний текст джерелаLi, Zhixiang, Liang He, and Yanjie Chu. "An Improved Decomposition Multiobjective Optimization Algorithm with Weight Vector Adaptation Strategy." In 2017 13th International Conference on Semantics, Knowledge and Grids (SKG). IEEE, 2017. http://dx.doi.org/10.1109/skg.2017.00012.
Повний текст джерелаHu, Shiqiang, and Zhongliang Jing. "A New Fusion Tracking Algorithm Based on Extended Adalines Model." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-42468.
Повний текст джерелаJunqueira, Paulo Pinheiro, Ivan Reinaldo Meneghini, and Frederico Gadelha Guimaraes. "Local Neighborhood-Based Adaptation of Weights in Multi-Objective Evolutionary Algorithms Based on Decomposition." In 2021 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2021. http://dx.doi.org/10.1109/cec45853.2021.9504688.
Повний текст джерела