Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/7872
標題: 針對動態系統處理的遞迴式自我演化的第二類型模糊類神經網路和其FPGA實現
A Recurrent Self-Evolving Interval Type-2 Fuzzy Neural Network for Dynamic System Processing and Its FPGA Implementation
作者: 林洋印
Lin, Yang -Yin
關鍵字: RSEIT2FNN;遞迴式的第二類型模糊類神經網路;Dynamic system;動態系統
出版社: 電機工程學系所
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摘要: 
本論文提出一個遞迴式自我演化的區間第二類型模糊類神經網路(RSEIT2FNN)針對動態系統處理,RSEIT2FNN 中的每一條遞迴的模糊規則的前件部都是第二類型的模糊集合而後件部是使用TSK型式的區間權重值。在架構中,RSEIT2FNN的前件部每個規則激發量形成一個內部反饋回到本身。 TSK類型後件部是外部輸入一個線性模型。RSEIT2FNN最初不包含規則,並且所有規則是經過網上架構學習和參數學習。架構學習是使用線上的第二類型模糊分群。在參數學習,後件部參數的學習是由排序後規則的Kalman濾波器演算法調整可具有高精確度學習的表現。前件部的參數和內部迴授的權重值則利用梯度下降法去學習。RSEIT2FNN的模擬在動態系統辨識和混沌和混亂信號預言在無噪聲和喧鬧的情況下。 與其他第一類型遞迴式模糊神經網絡的比較驗証RSEIT2FNN的性能。

This paper proposes a Recurrent Self-Evolving Interval Type-2 Fuzzy Neural Network (RSEIT2FNN) for dynamic system processing. The antecedent parts in each recurrent fuzzy rule of the RSEIT2FNN are interval type-2 fuzzy sets and the consequent part is of the Takagi-Sugeno-Kang (TSK) type with interval weights. In structure, the antecedent part of RSEIT2FNN forms a locally internal feedback loop by feeding the rule firing strength of each rule back to itself. The TSK-type consequent part is a linear model of exogenous inputs. The RSEIT2FNN contains no rules initially and all rules are learned on-line via structure and parameter learning. Structure learning uses on-line type-2 fuzzy clustering. For parameter learning, the consequent part parameters are tuned by a rule-ordered Kalman filter algorithm for high accuracy learning performance. The antecedent type-2 fuzzy sets and internal feedback loop weights are learned by a gradient descent algorithm. The RSEIT2FNN is applied to simulations on dynamic system identifications and chaotic signal prediction under noise-free and noisy conditions. Comparisons with other type-1 recurrent fuzzy neural networks verify the performance of the RSEIT2FNN.
URI: http://hdl.handle.net/11455/7872
其他識別: U0005-0508200815204600
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