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標題: 新穎的前向式與遞迴式第二類型模糊類神經網路設計和FPGA實現
Novel Approaches For Feedforward / Recurrent Interval Type-2 Fuzzy Neural Network Design and FPGA Implementation
作者: 黃仁伯
Huang, Ren-Bo
關鍵字: Feedforward;前向式;Recurrent;Type-2;Fuzzy Neural Network;遞迴式;模糊類神經網路
出版社: 電機工程學系所
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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。

This 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.
其他識別: U0005-1808200914260500
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