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研究生:張廷謙
研究生(外文):Chang, Ting-Chein
論文名稱:多階推理模糊神經網路之結構設計
論文名稱(外文):The Structure Design for a Multi-level Fuzzy Neural Network
指導教授:譚建民, 單智君
指導教授(外文):Jiann-Mean Tan, Jyh-Jian Shann
學位類別:碩士
校院名稱:國立交通大學
系所名稱:資訊科學學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:1996
畢業學年度:84
語文別:中文
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本篇論文提出一種多階推理模糊神經網路, 用以學習多階推理的模糊
規則與歸屬函數. 多階推理模糊神經網路是由單階推理模糊神經網路延
伸, 修改而得, 其架構符合多階模糊推理程序.節點函數由 Lasen's MAX-
PRODUCT 定義而來. 學習方法是基於回傳式(backpropagation)學習, 並
經由適當的刪除, 得到精簡的模糊規則庫. 針對此多階推理模糊神經網
路, 以三階段討論網路的學習情況. 1.誤差回傳訓練期(the Error
Backpropagation Training, EBP-Training, Phase) 2.歸屬函數刪
減期(the Membership Function Pruning, MF-Pruning, Phase)
3.模糊規則刪減期(the Rule Pruning, R-Pruning, Phase)在誤差回傳訓
練期, 多階推理模糊神經網路以回傳式演算,來學得知識. 在歸屬函數刪
減期與模糊規則刪減期, 刪減多餘的歸屬函數與模糊規則, 以求得精簡的
知識庫. 實驗結果顯示, 經過三階段的學習程序, 可獲得精簡的模糊規
則.

In this dissertation, a multi-level fuzzy neural network
isproposed for learning the firing strengths of fuzzy rules and
thebell-shaped membership functions for the linguistic values of
inputand output linguistic variables. The proposed network is
extendedfrom single-level fuzzy neural network. Basically,
structure of proposed network also corresponds to multi-level
inference procedure. The node function is designed from lasen's
MAX-PRODUCT inference.The backpropagation learning algorithm is
adopted to train the learnable parameters. After the deletion of
these rules, a fuzzy rule base with precise knowledge and small
size can be obtained.A procedure consisting of three different
phase for learning the knowledge of multi-level fuzzy neural
network is proposed. The threephase are :(1).the Error
Backpropagation Training (EBP-Training) Phase,(2).the Membership
Function Pruning(MF-Pruning) Phase, and(3).the Rule-Phase(R-
Pruning).The learning algorithms in the training phase train the
learnableparameters based on the gradient descent concept of
backpropagationlearning algorithm. After the training, the MF-
Pruning and R-Pruningphase are performanced to delete redundant
membership functions andfuzzy rules. Simulation results show
three different phase for learningthe knowledge of multi-level
fuzzy neural network, a fuzzy rule base with precise knowledge
and small size can be obtained.

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