This paper proposes a hierarchical learning algorithm for optimizing fuzzy rule bases.
提出了一种二层学习算法来优化模糊规则基。
An algorithm for optimizing the node number of the numerical simulation of sheet metal forming processes is presented.
提出一种用于板料成形数值模拟的网格结点编号优化算法。
A single behavior object contains the algorithm for optimizing the demonstrated group of ACTS. The algorithm is using the Q-learning based on artificial nerve network.
在单独的行为对象中包含了基于强化学习中的Q学习及人工神经网络的优化学习算法。
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