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摘要: 提出了一种高阶模糊小脑模型神经网络控制器(HOFCMAC),利用模糊子集对输 入状态空间进行分割,同时采用多层的量化方式对输入状态进行量化,并利用代数积,代数和 的方法综合各种量化方式的量化结果.由于多层量化方式的应用,这种控制器也比单纯基于 广义基函数的模糊CMAC有更好的控制性能.复杂工业炉温控制试验结果证明这种方法的 有效性.Abstract: This paper presents a High-Order Fuzzy CMAC adaptive control, using the fuzzy subset to partition input state spaces, employing the multi-layer quantification to quantify input states, and utilizing the algebraic product and algebraic sum to integrate the result of multi-layer quantification. The controller has better capability of generalization than the ordinary CMAC. Due to the use of multi-layer quantification, the controller has better control performance than that simply based on the generalized basic function fuzzy CMAC. Temperature control experiments on an industrial furnace prove the effectiveness of the proposed method.
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