臺灣博碩士論文加值系統

本篇論文提出一自我建構之模糊小腦模型神經網路及其應用。在自我建構之模糊小腦模型神經網路中，高斯基底函數用於建構容許區域函數及模糊權重函數。另外，為了加強執行效能，以自我建構之模糊小腦模型神經網路為基礎，發展出一自我建構之參數型模糊小腦模型神經網路，其中，高斯基底函數用於建構容許區域函數，而輸入變數的線性組合方程式則用於建構TSK 型態輸出向量。上述兩個所建議的模組，對於時間相關的問題有所侷限，無法有效的表現出物件動向，所以發展出一自我建構之遞歸型模糊小腦模型神經網路，在其中，加入遞歸型網路於容許區域函數中與之相連結，其原理是存在一回授單元用來當作記憶單元，以儲存每次訓練經容許區域函數計算所得之輸出。線上學習演算法用以自動建構此三種所建議的模組，此學習演算法包含架構學習及參數學習。架構學習利用程度測量完成，而參數學習則利用倒傳遞演算法完成。將此三種所建議的模組以多個不同領域的範例來作軟體模擬，模擬後的結果即可證明此三種模組的執行效能及適用性。

In this thesis, a self-constructing fuzzy CMAC (FCMAC) model is proposed for various different applications. The Gaussian basis function is used to model the receptive field functions and the fuzzy weights for the FCMAC model. In order to make the better performance, we develop a self-constructing parametric fuzzy CMAC (called PFCMAC) model, based on the FCMAC model, which the Gaussian basis function is used to model the receptive field functions and the linear parametric equation of the model input variance is used to model the TSK-type outputs. Besides, if these two proposed model’s application domain is limited to static problems as a result of their internal feedforward network structure. To process temporal problems using these two proposed model are inefficient. Then the additional task is adopted which is the recurrent network is embedded in the FCMAC model by adding feedback connections with a receptive field cell to the FCMAC model, where the feedback units act as memory elements that can form the self-constructing recurrent fuzzy CMAC (called RFCMAC) model. An on-line learning algorithm is proposed to automatically construct the FCMAC model, the PFCMAC model, and the RFCMAC model, which consists of a structure learning scheme and a parameter learning scheme. The structure learning is based on the degree measure and the parameter learning is based on backpropagation algorithm. Finally, these three proposed models are applied in several simulations. Simulation results were conducted to illustrate the performance and applicability of these three proposed models.

Abstract in Chinese I
Abstract in English III
Acknowledgements in Chinese V
Contents VI
List of Tables VIII
List of Figures IX
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 The CMAC Model 6
1.3 Organization of This Thesis 9
Chapter 2 The Self-Constructing Fuzzy CMAC Model 11
2.1 Introduction 11
2.2 The Structure of the Self-Constructing Fuzzy CMAC Model 12
2.3 An On-Line Learning Algorithm for the FCMAC Model 15
2.3.1 The Structure Learning Scheme 16
2.3.2 The Parameter Learning Scheme 17
2.4 Simulation Results 20
2.5 Conclusion 24
Chapter 3 The Self-Constructing Parametric Fuzzy CMAC Model 25
3.1 Introduction 25
3.2 The Structure of the Self-Constructing Parametric Fuzzy CMAC Model 26
3.3 An On-Line Learning Algorithm for the PFCMAC Model 29
3.3.1 The Structure Learning Scheme 30
3.3.2 The Parameter Learning Scheme 30
3.4 Simulation Results 32
3.5 Conclusion 40
Chapter 4 The Self-Constructing Recurrent Fuzzy CMAC Model 41
4.1 Introduction 41
4.2 The Structure of the Self-Constructing Recurrent Fuzzy CMAC Model 43
4.3 An On-Line Learning Algorithm for the RFCMAC Model 46
4.3.1 The Structure Learning Scheme 47
4.3.2 The Parameter Learning Scheme 47
4.4 Simulation Results 49
4.5 Conclusion 60
Chapter 5 Conclusion 61
Bibliography 63
Vita 72
Publication List 73

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