中文摘要：

将模糊控制和神经网络相结合,形成模糊神经网络（fuzzy neural network,FNN）控制器,利用联想记忆进行离线训练,用来记忆预先利用强化Q学习（Q-Learning,QL）在线训练获得的移动机器人自适应行为的模糊控制规则。FNN经过离线训练后,把规则隐含地分布在整个网络之中,在控制应用时,不必进行复杂的规则搜索和推理,无需查表,只需通过高速并行的分布计算就可产生最佳输出的自适应行为。仿真结果表明,由于输入模糊子集接近于网络所用的训练模糊子集,所以输出几乎和该条训练规则的结果相同。

英文摘要：

The fuzzy neural network（FNN）controller was formed by combining the fuzzy control and the neural networks.The fuzzy rules were collected automatically by reinforcement Q-Learning（QL）on-line beforehand.The rules were implicitly stored in the FNN after the off-line training.In control applications,the complex search and inference of the rules were unnecessary,and the best adaptive behavior could be produced in the output without looking up the table.The simulation results show that because all training samples are from the trained fuzzy rules,the output is almost the same as the result of the training rules.