Jan 29, 2019 · Given a multi-stage optimal-control problem, Liao [11] reformulated it as the terminal-state cost problem via classical transformation [2], and ...
This paper claims that BP on the transformed problem leads to new recurrent neural network learning, and highlights systematic BP derivations by employing ...
A Note on Liao's Recurrent Neural-Network Learning for ...
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The roots of neural-network backpropagation (BP) may be traced back to classical optimal-control gradient procedures developed in early 1960s.
A neural network model is proposed in this paper for the discrete-time optimal control problem with control constraints based on the projection method and ...
A Note on Liao's Recurrent Neural-Network Learning for Discrete Multi-stage Optimal Control Problems. Eiji Mizutani. BriefCommunication 29 January 2019 Pages ...
A Note on Liao's Recurrent Neural-Network Learning for Discrete Multi-stage Optimal Control Problems. Mizutani, Eiji et al. | 2019. digital version. 3019. A ...
A Note on Liao's Recurrent Neural-Network Learning for Discrete Multi-stage Optimal Control Problems. Article. Jan 2019. Eiji Mizutani. The roots ...
A recurrent neural network is introduced for the N-stage optimal control problem. The new neural network is based on a reformulation of the original optimal ...
Missing: Discrete Multi-
Aug 6, 2024 · A Note on Liao's Recurrent Neural-Network Learning for Discrete Multi-stage Optimal Control Problems. Neural Process. Lett. 50(3): 3009-3018 ...
A Note on Liao's Recurrent Neural-Network Learning for Discrete Multi-stage Optimal Control Problems. Mizutani, Eiji. Neural processing ...