Hallmark
发表于 2025-3-23 11:53:57
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敏捷
发表于 2025-3-23 15:26:04
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Oligarchy
发表于 2025-3-23 18:26:29
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刺激
发表于 2025-3-23 23:07:04
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Atrium
发表于 2025-3-24 03:22:29
Some Preliminary Comparisons Between a Neural Adaptive Controller and a Model Reference Adaptive Conple and well defined first-order problem. We focus on the rate of convergence, and on the capacity to control non-linear and time-varying systems. Results from a first experiment show that the MRAC always converges faster and performs better for linear systems, but that its performances decline in c
大洪水
发表于 2025-3-24 07:17:57
Stable Nonlinear System Identification Using Neural Network Models a stability theory approach to synthesizing and analyzing neural network based identification schemes. First static network architectures are combined with dynamical elements in the form of stable filters to construct a type of recurrent network configuration which is shown to be capable of approxi
Microgram
发表于 2025-3-24 12:19:17
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Circumscribe
发表于 2025-3-24 16:03:28
Neural Networks Learning Rules for Control: Uniform Dynamic Backpropagation, Heavy Adaptive Learningms. The Uniform Dynamic BackPropagation rule is based on a non regular optimization scheme (subgradient algorithm), and is devoted to the minimisation of a Min Max criterion, in a neural network synaptic matrix space. The Heavy Adaptive learning rule is a continuous hebbian learning rule, enabling a
掺假
发表于 2025-3-24 20:35:40
Parameter Learning and Compliance Control Using Neural Networks [.], the shortcomings of current control methods in dealing with such applications were elucidated, and a new robust control approach based on terminal sliding modes was introduced. In this paper, the problem of identifying uncertain environments for stable contact control is considered. For the pu
谎言
发表于 2025-3-25 01:50:14
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