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Reinforcement learning (RL) is a popular learning paradigm to adaptive learning control of nonlinear systems, and is able to work without an explicit model. However, learning from scratch, i.e., without any a priori knowledge, is a daunting undertaking, which results in long training time and instability of learning process with large continuous state space. For physical systems, one must consider...
In the conventional variable universe fuzzy control system, it was hard to select the function models of the contraction-expansion factors and parameters of the models. A new variable universe fuzzy control algorithm, to achieve the contraction-expansion of universes by fuzzy neural network in place of the function models, was proposed in this paper. The algorithm, with the semantic characteristics...
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