Results 281 to 290 of about 1,466,211 (327)

Iterative learning control design based on feedback linearization and nonlinear repetitive process stability theory

2016 IEEE 55th Conference on Decision and Control (CDC), 2016
Iterative learning control laws can be applied to systems that execute the same finite duration task over and over again. Previous research for linear dynamics has used the stability theory of linear repetitive processes to design control laws that have been experimentally verified.
Pavel Pakshin, Julia Emelianova, Mikhail Emelianov, Krzysztof Gałkowski, Eric Rogers   +4 more
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Performance Approach-Avoidance Motivation and Planned Behavior Theory: Model Stability with Greek Students with and without Learning Disabilities

Reading & Writing Quarterly, 2005
ABSTRACT Two studies evaluated the contribution of goal orientation—over and above the constructs of planned behavior theory—in explaining the relationship between attitudes, motivation, and academic achievement for students with and without learning disabilities.
Georgios D. Sideridis
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DIM-DS: Dynamic Incentive Model for Data Sharing in Federated Learning Based on Smart Contracts and Evolutionary Game Theory

IEEE Internet of Things Journal, 2022
With the development of big data, data sharing has become a hot topic. According to the previous research on data sharing, there is a problem with regard to how to design an effective incentive mechanism to make users willing to share data.
Yanru Chen, Yuanyuan Zhang, S. Wang, Fan Wang, Y. Li, Yuming Jiang, Liangyin Chen, Bing Guo   +7 more
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Reliability Evaluation Method of Power Grid Security and Stability Control System Based on Survival Theory and Deep Learning

2021 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia), 2021
Weiwei Cai, Yuxin Shi, Weiheng Han, Lijia Ren, Ting Lei, Hourui Liu, Jingchi Xie, Sheng Huang   +7 more
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Research Review for Broad Learning System: Algorithms, Theory, and Applications

IEEE Transactions on Cybernetics, 2021
In recent years, the appearance of the broad learning system (BLS) is poised to revolutionize conventional artificial intelligence methods. It represents a step toward building more efficient and effective machine-learning methods that can be extended to
Xinrong Gong, Ieee Tong Zhang Member, Ieee C. L. Philip Chen Fellow, Ieee Zhulin Liu Member   +3 more
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Deep Lyapunov Learning: Embedding the Lyapunov Stability Theory in Interpretable Neural Networks for Transient Stability Assessment

IEEE Transactions on Power Systems
Jiacheng Liu, Jun Liu, Rudai Yan, Tao Ding   +3 more
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On a unique solution and stability analysis of a class of stochastic functional equations arising in learning theory

Analysis, 2022
Abstract Numerous computational and learning theory models have been studied using probabilistic functional equations. Especially in two-choice scenarios, the vast bulk of animal behavior research divides such situations into two different events. They split these actions into two possibilities according to the animals’ progress toward a
Ali Turab
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Static Voltage Stability Margin Evaluation Considering Stochastic Power Injection Based on Deep Reinforcement Learning Theory

2022 IEEE 5th International Electrical and Energy Conference (CIEEC), 2022
Yi Yun Tian, Jun Yi Zhang, Wen Huang, Ceng Lin Ruan, Yi Chao Wu, Li Xia Sun   +5 more
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A Test Oracle for Reinforcement Learning Software Based on Lyapunov Stability Control Theory

2025 IEEE/ACM 47th International Conference on Software Engineering (ICSE)
Shiyu Zhang, Haoyang Song, Qixin Wang, Henghua Shen, Yu Pei   +4 more
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Further results on dynamic iterative learning control law design using repetitive process stability theory

2017 10th International Workshop on Multidimensional (nD) Systems (nDS), 2017
Iterative learning control can be applied to systems that execute the same finite duration task over and over again. This method control has been applied to many engineering systems, such as gantry robots and electrical motors. This paper gives further results on the design of dynamic iterative learning control laws using the repetitive process setting
Łukasz Hładowski, Krzysztof Gałkowski, Eric Rogers   +2 more
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