Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/6637
標題: 可解釋的/簡化的第二類型模糊類神經網路和具有學習能力之FPGA實現
Interpretable/Simplified Type-2 Fuzzy Neural Networks and FPGA Implementation with Learning Ability
作者: 陳奇佑
Chen, Chi-You
關鍵字: type-2 fuzzy systems;第二類型模糊系統;fuzzy chip;fuzzy neural;模糊芯片;模糊類神經網路
出版社: 電機工程學系所
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摘要: 
本論文提出了一種以數據驅動具改善解釋性的間隔第二類型模糊類神經網絡(DIT2FNN-I)。DIT2FNN- I在前件部使用區間的第二類型模糊集合,而後件部使用零階的Takagi-Sugeno-Kang (TSK)模糊規則型式。此架構和最初的規則庫是由輸入輸出空間上的自我分裂分群演算法產生。DIT2FNN- I 使用兩階段的參數學習演算法去設計一個具有改善規則解釋性的精確模組。第一階段定義了一個新的具有語意限制的成本函數,其同時考慮準確性和模糊集合區分的透明性。在學習的過程中,限制了模糊集合調整以便區分分組且每個輸入變數值域是被強烈覆蓋。前件部和後件部的參數是分別藉由梯度下降法和卡門濾波器演算法來學習以求最小化成本函數。第二階段執行模糊集合減化,然後藉由後件部參數學習以改進準確性。本文以DIT2FNN- I來應用五個以數據驅動的模組和預測問題。在這些例子中,比較不同第一類型和第二類型系統來驗證DIT2FNN- I模組的準確性和可解釋性的表現。
本論文還提出了一種簡化第二型模糊類神經網以利於適應性硬體(ST2FNN- AH)實現。該 ST2FNN- AH晶片採用可程式邏輯閘陣列(FPGA)來實現。ST2FNN- AH前件部使用第二類型的模糊集合,而後件部使用零階TSK模糊規則型式。ST2FNN- AH使用簡化的降型計算,以降低硬體執行成本。ST2FNN- AH後件部參數學習以梯度下降法調整,此學習能力並實現於硬體電路中。此具有線上參數學習能力的ST2FNN- AH晶片,可用來執行時變系統的判別與控制問題。論文並以數個實驗結果驗證ST2FNN- AH晶片的正確性與學習能力。

This thesis proposes a data-driven interval type-2 fuzzy neural network with improved interpretability (DIT2FNN-I). The DIT2FNN-I uses interval type-2 fuzzy sets in its antecedent part and intervals in its zero-order Takagi-Sugeno-Kang (TSK)-type consequent part for rule form simplicity. The structure and initial rule-base is generated by a self-splitting clustering algorithm in input-output space. The DIT2FNN-I uses a two-stage parameter learning algorithm to design an accurate model with improved rule interpretability. In the first stage, a new cost function with semantic constraints that considers both accuracy and transparent fuzzy set partition is defined. Constraints are imposed on the fuzzy sets during learning so that they are distinguishably grouped and the universe of course of each input variable is strongly covered. The antecedent and consequent parameters are learned by gradient descent and rule-ordered Kalman filter algorithms, respectively, for cost function minimization. The second stage performs a fuzzy set reduction followed by consequent parameter learning to improve accuracy. The paper applies the DIT2FNN-I to five data-based modeling and prediction problems. Comparisons with different type-1 and type-2 fuzzy systems in these problems verify the performance of the DIT2FNN-I on both model accuracy and interpretability.
This thesis also proposes a simplified type-2 fuzzy neural network for adaptive hardware implementation (ST2FNN-AH). The ST2FNN-AH chip is implemented using field-programmable gate array (FPGA). The antecedent part in each fuzzy rule of ST2FNN-AH uses interval type-2 fuzzy sets and the consequent part is zero-order Takagi-Sugeno-Kang (TSK) type. The ST2FNN-AH uses a simplified type-reduction operation to reduce hardware implementation cost. The consequent parameters are tuned using the gradient descent algorithm, and the learning algorithm is implemented in the ST2FNN-AH chip. The ST2FNN-AH chip is characterized with online parameter learning ability, and therefore, can be applied to identification and control of time-varying systems. Several experiments are conduced to verify the effectiveness and learning ability of the ST2FNN-AH chip.
URI: http://hdl.handle.net/11455/6637
其他識別: U0005-1708201113444400
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