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dc.description.abstract本論文提出一個新穎的前向式區間第二類型模糊類神經網路,其設計基於支持向量回歸法(IT2FNN-SVR),IT2FNN-SVR使用Takagi-Sugeno-Kang (TSK)模糊規則形式,IT2FNN-SVR中的每一條模糊規則的前件部使用第二類型模糊集合。網路輸入端分為實數值與Type-1模糊集合輸入,後者被用來增進抵抗雜訊能力。IT2FNN-SVR包含架構學習以及參數學習部分,在架構學習中,架構學習演算法運用於線上產生模糊規則。參數學習中,後件部參數運用支持向量回歸法學習並賦予網路高度的綜合能力。在雜訊回歸問題上,與Type-1,Type-2模糊類神經系統及Gaussian-kernel SVR比較來證明IT2FNN-SVR的效能的優化。 本論文也提出一個新的遞迴式區間第二類型模糊類神經網路(RIT2FNN)針對動態系統建立。另外對於RIT2FNN利用可程式邏輯閘陣列(FPGA)晶片實現上提出一個新的硬體實現技術。不同於現有的遞迴式Type-1模糊系統,RIT2FNN的前件部與後件部都是利用Type-2模糊集合來增進網路抗雜訊能力。RIT2FNN經由架構學習及參數學習來建立一個新的遞迴式架構伴隨著遞迴迴路來處裡動態系統問題。在於RIT2FNN硬體實現上,pipeline技術跟針對Type簡化運算的新電路也被提出來增進晶片的效能。模擬與比較上與遞迴式Type-1模糊類神經網路和前向式Type-2模糊類神經網路驗證RIT2FNN的效能。zh_TW
dc.description.abstractThis thesis proposes a novel feedforward interval type-2 fuzzy neural network, an Interval Type-2 Fuzzy Neural Network with Support Vector Regression (IT2FNN-SVR). The antecedent part in each fuzzy rule of an IT2FNN-SVR uses interval type-2 fuzzy sets and the consequent part is Takagi-Sugeno-Kang (TSK)-type. The networks inputs may be numerical values or type-1 fuzzy sets, where the latter is used to improve network noise resistance ability. The IT2FNN-SVR learning consists of structure learning and parameter learning. The structure learning algorithm is responsible for on-line rule generation. After structure learning, the parameters are learned through linear support vector regression (SVR) to endow the network high generalization ability. Performance of IT2FNN-SVR is verified through comparisons with those of neural networks, type-1, type-2 neural fuzzy systems, and Gaussian-kernel SVR on noisy regression problems. This paper also proposes a new recurrent interval type-2 fuzzy neural network (RIT2FNN) for dynamic system modeling. A new hardware implementation technique for the RIT2FNN using a field-programmable gate array (FPGA) chip is then proposed. Unlike existing recurrent fuzzy systems that focus on the use of type-1 fuzzy sets, the antecedent and consequent parts in a RIT2FNN use interval type-2 fuzzy sets in order to increase the network noise resistance ability. A new recurrent structure is proposed in RIT2FNN, with the recurrent loops enabling it to handle dynamic system processing problems. A RIT2FNN is constructed from structure and parameter learning. For hardware implementation of the RIT2FNN, the pipeline technique and a new circuit for type reduction operation are proposed to improve the chip performance. Simulations and comparisons with other recurrent type-1 fuzzy neural networks and feedforward type-2 fuzzy neural networks verify the performance of the RIT2FNN under noisy conditions.en_US
dc.description.tableofcontentsAbstract (in Chinese)--i Abstract (in English)--ii Acknowledgements--iii Contents--iv List of Figures--vi List of Tables--viii Chapter 1 : Introduction--1 1.1 Survey And Literature Review--1 1.1.1 Feedforward type-2 Fuzzy Systems--1 1.1.2 Recurrent Type-2 Fuzzy Systems--3 1.1.3 Hardware Implementation of Type-2 Fuzzy Systems--4 1.2 Organization of the Thesis--5 Chapter 2 : Feedforward Interval Type-2 Fuzzy Neural Network--6 2.1 Structure of IT2FNN-SVR--6 2.2 Structure Learning of IT2FNN-SVR--11 2.3 Parameter Learning of IT2FNN-SVR--12 2.3.1 Basic SVR concepts--12 2.3.2 Parameter Learning--15 Chapter 3 : Recurrent Interval Type-2 Fuzzy Neural Network--20 3.1 Structure of RIT2FNN--20 3.2 Structure Learning of RIT2FNN--24 3.3 Parameter Learning of RIT2FNN--26 Chapter 4 : FPGA Implementation of RIT2FNN--30 Chapter 5 : Simulations and Experiments--36 5.1 Simulation results of IT2FNN-SVR--36 5.1.1 Simulations--36 5.1.2 Dicussions--48 5.2 Simulation results of RIT2FNN--50 5.3 Experimental results on FPGA Implementation--55 Chapter 6 : Conclusions--59 Bibliography--60en_US
dc.subjectFuzzy Neural Networken_US
dc.titleNovel Approaches For Feedforward / Recurrent Interval Type-2 Fuzzy Neural Network Design and FPGA Implementationen_US
dc.typeThesis and Dissertationzh_TW
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item.openairetypeThesis and Dissertation-
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