Gastric
发表于 2025-3-27 00:44:47
https://doi.org/10.1007/978-981-13-1712-5Optimal control; Multi-player games; Adaptive dynamic programming; Nonlinear systems; Neural network-bas
finale
发表于 2025-3-27 04:17:07
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forecast
发表于 2025-3-27 06:22:11
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Chandelier
发表于 2025-3-27 12:37:46
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袖章
发表于 2025-3-27 16:48:45
https://doi.org/10.1007/978-3-031-46375-4he weighted sum technology, the original multi-objective optimal control problem is transformed to the single one. An ADP method is established for nonlinear time-delay systems to solve the optimal control problem. To demonstrate the presented iterative performance index function sequence is converg
hematuria
发表于 2025-3-27 21:13:06
https://doi.org/10.1007/978-3-031-46375-4tuation, this chapter proposes multiple actor-critic structures to obtain the optimal control via input-output data for unknown nonlinear systems. The shunting inhibitory artificial neural network (SIANN) is used to classify the input-output data into one of several categories. Different performance
口音在加重
发表于 2025-3-28 00:38:59
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Decibel
发表于 2025-3-28 05:04:04
Polyphony: Authorship and Power,obi–Bellman (HJB) equation. Off-policy learning allows the iterative performance index and iterative control to be obtained by completely unknown dynamics. Critic and action networks are used to get the iterative control and iterative performance index, which execute policy evaluation and policy imp
适宜
发表于 2025-3-28 06:18:39
Lakshmi Bandlamudi,E. V. Ramakrishnangorithm. Via the system transformation, the optimal tracking problem is transformed into an optimal regulation problem, and then the novel optimal tracking control method is proposed. It is shown that for the iterative ADP algorithm with finite approximation error, the iterative performance index fu
backdrop
发表于 2025-3-28 14:23:37
Bakhtinian Explorations of Indian Cultureing (IRL) algorithm is presented to obtain the iterative control. Off-policy learning is used to allow the dynamics to be completely unknown. Neural networks (NN) are used to construct critic and action networks. It is shown that if there are unknown disturbances, off-policy IRL may not converge or