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標題: 以分段法建構模糊規則為基礎之倒傳遞神經網路
Fuzzy Rule Based Backpropagation Neural Network Constructed by Divide-and-Conquer Technique
作者: 陳舜賢
關鍵字: 類神經模糊網路;倒傳遞神經網路;TSK(Takagi-Sugeno-Kang);模糊群集;架構學習;參數學習
出版社: 電機工程學系

This thesis proposes a novel neural fuzzy network, the Fuzzy Rule Based Back-Propagation Neural Network (FRBPNN), constructed based on the divide-and-conquer technique. In fuzzy rule form, the FRBPNN is of Takagi-Sugeno-Kang (TSK)-type, where the consequence is a BPNN. The architecture design of FRBPNN employs the concept of fuzzy clustering that divides the input training data to different clusters, and the input-output mapping of each cluster is learned by a BPNN. In FRBPNN, once a new fuzzy rule is built, a corresponding BPNN will be built successively.
Learning of FRBPNN is based on simultaneous structure and parameter learning. Initially, the rule base is blank. All of the rules are constructed on-line by fuzzy clustering. For a newly generated rule, a criterion is proposed to determine whether a new fuzzy set should be generated on each input variables. This way we can reduce the number of fuzzy sets. The structure of BPNN at the consequence of each new fuzzy rule can be assigned in advance or automatically built. For automatic building, the initial BPNN contains only one hidden node. When the learning error decreasing rate is not satisfied over a period of time, a new node is added to the BPNN in the rule with the largest distributed error. For parameter learning, all of the free parameters in the BPNN and precondition part of the fuzzy rules are learned by gradient descent.
To measure the performance of FRBPNN, four examples are simulated. Performance of FRBPNN is compared with BPNN and other type of neural fuzzy network to verify its superiority.
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