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標題: 以模糊規則為基礎自我組織建構之類神經網路
A Self-Organizing Fuzzy Rule-Based Neural Network
作者: 林文揚
Lin, Wen-Yang
關鍵字: Neural;類神經
出版社: 電機工程學系所
引用: [1] Kalman, R. E. . “A New Approach to Linear Filtering and Prediction Problems,” Transaction of the ASME—Journal of Basic Engineering, pp. 35-45 (March 1960). [2] C. T. Lin and C. S. G. Lee “Neural Fuzzy System: A Neuro-Fuzzy Synergism to Intelligent Systems,”Prentice Hall International, Inc., (1996) [3] T. Back, “ Evolutionary Algorithms in Theory and Practice,”Oxford University Press, New York, (1996). [4] C. T. Lin, “Neural Fuzzy Control System with Structure and Parameter Learning,” New York: World Scientific, (1994). 5] M. Russo, “ Genetic fuzzy learning,” IEEE Trans. Evolutionary Comput. 4 pp. 259–273.(2000) [6] C.F. Juang, J.Y. Lin, C.T. Lin, “Genetic reinforcement learning through symbiotic evolution for fuzzy controller design,” IEEE Trans. Systems Man. and Cybernetics, Part B: Cybernetics ,vol.30 (2) pp 290–302. (2000) [7] D. Kim, C. Kim, “Forecasting time series with genetic fuzzy predictor ensemble,” IEEE Trans. Fuzzy Systems vol. 5 (4)pp.523–535. (1997) [8] J. S. Jang, “ANFIS Adaptive-network-based fuzzy inference system,” IEEE rans. Syst., Man, Cybern., vol. 23, pp. 665-685, (May 1993). [9] A. Lotfi and A. C. Tsoi, “ Learning Fuzzy Inference Systems Using an Adaptive Membership Function Scheme,” IEEE Trans on Systems, vol. 26, no. 2,( April 1996) [10] C.F. Juang, C.T. Lin, “An on-line self-constructing neural fuzzy inference network and its applications,” IEEE Trans. Fuzzy Systems vol.6 pp12–32. (1998) [11] S. Horikawa, T. Furuhashi, and Y. Uchikawa, “On fuzzy modeling using fuzzy neural networks with the backpropagation algorithm,” IEEE Trans. Neural Networks, vol. 3, pp. 801–806, (Sept. 1992). [12] C. F. Juang, I-Fang Chung, C. H. Hsua, “Automatic construction of feedforward/recurrent fuzzy systems by clustering-aided simplex particle swarm optimization” Fuzzy Sets and Systems 158 pp. 1979–1996(2007) [13] C. T. Lin and C. S. G. Lee, “Neural-network-based fuzzy logic control and decision system,” IEEE Trans. Comput., vol. 40, pp. 1320–1336,( Dec. 1991). [14] 陳舜賢“以分斷法建構模糊規則為基礎之倒傳遞神經網路” 中興大學電機研究所碩士論文,(2005) [15] C. T. Lin, C.S.George Lee “Neural Fuzzy System-A Neural Fuzzy Synerism to Intelligent System” Chiao-Tung Univerity Hsinchu ,Taiwan, Univerity Lafayette,Indiana, pp533-.608(Aug 2003)
我組織神經網路(A Self-Organizing Fuzzy Rule-Based Neural Network-SOFRBNN)。 以Takagi-Sugeno-Kang (TSK) 形式模糊法則為基礎,整個網路包含兩部份,前件部與後件部。其中每條法則後件部是由以卡爾曼濾波器(Kalman filer)[1],訓練的類神經網路 (KFNN)構成。KFNN結構包含三層。整個SOFRBNN網路初期是無模糊法則存在,法則是依輸入資料經線上演算而建立。當新建立一條法則,後件部相對建立一個KFNN。在KFNN參數調整上,第二、三層間參數是以卡爾曼濾波器調整,而第一、二層間參數是以梯度下降法調整。SOFRBNN的特性是網路架構建立及參數學習是同時進行,所以能對網路作有效的學習。本論文將對混沌訊號估測及非線性系統判別問題作模擬,以驗證其學習效果。

This thesis proposes a new fuzzy neural network, the Self-Organizing Fuzzy Rule -Based Neural Network (SOFRBNN). Based on Takagi-Sugeno-Kang (TSK) type fuzzy rules, the network consists of two parts, the antecedent and the consequent parts. A Kalmam filer trained Neural Network (KFNN) constitutes the consequent part of each rule, where a KFNN comprises three layers. There are no rules in SOFRBNN initially as rules are on-line generated according to training data. Once a new rule is generated, a new KFNN is generated accordingly in the consequent part. For KFNN parameter learning, the parameters between layers two and three are tuned using Kalman filter, while the parameters between layers one and two are tuned using gradient descent algorithms. The antecedent part parameters in SOFRBNN are also tuned using gradient descent algorithms. SOFRBN characterizes concurrent structure and parameter learning, and good learning performance accompanies this characteristic. This thesis performs simulations on chaotic signal prediction and nonlinear system identification to verify SOFRBNN performance.
其他識別: U0005-3001200813590200
Appears in Collections:電機工程學系所

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