跳到主要內容

臺灣博碩士論文加值系統

(18.97.9.172) 您好!臺灣時間:2025/02/14 04:33
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:高子晉
研究生(外文):Tzu-Chin Kao
論文名稱:一種用於分類與迴歸建模的改良式模糊極限學習機
論文名稱(外文):An Improved Fuzzy Extreme Learning Machine for Classification and Regression Modeling
指導教授:歐陽振森
指導教授(外文):Chen-Sen Ouyang
學位類別:碩士
校院名稱:義守大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2016
畢業學年度:104
語文別:中文
論文頁數:40
中文關鍵詞:改良式極限學習機極限學習機分類迴歸妥協式運算元模糊規則類神經網路奇異值分解
外文關鍵詞:Improved fuzzy extreme learning machinExtreme learning machineClassificationregressionCompensatory operatorFuzzy ruleNeural networkSingular value decomposition
相關次數:
  • 被引用被引用:0
  • 點閱點閱:275
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
本研究針對分類與迴歸建模問題,提出一種改良式模糊極限學習機。此方法主要改良自Wong [2]等人所提出的模糊極限學習機,針對其推廣能力與靈活性易受限制的問題,我們將其常數形態之模糊規則結論推廣為第一階TSK形態,並以妥協式運算元取代其一般化AND運算元。此外,我們亦運用基於遞迴式奇異值分解之最小平方估計值來取代其廣義逆矩陣求解方式,藉以適用於時變系統迴歸建模。為了驗證本研究方法的可行性與優越性,我們與其他方法於分類、時變或非時變系統迴歸建模結果進行比較。實驗結果顯示,本研究所提出改良式模糊極限學習機無論在分類的正確率或迴歸的均方誤差皆有較好且較穩定之表現。

In this study, we propose an improved fuzzy extreme learning machine for classification and regression modeling. This approach is mainly an improved version of the fuzzy extreme learning machine proposed by Wong et al. [2]. To alleviate the restricted generalization capability and flexibility problem encountered in Wong’s approach, the original type of rule consequent part, i.e., zero-order Takagi-Sugeno-Kang (TSK) type, is extended to the first-order TSK type, and the original T-norm fuzzy operator, i.e., generalized AND, is replaced by a compensatory fuzzy operator. Besides, the original Moore–Penrose generalized inverse is replaced with a recursive SVD-based least square estimator for solving the problems of regression modeling of time-variant systems. To verify the feasibility and superiority of our approach, we perform several experiments on modeling problems of classification and time-invariant or time-variant systems and make a comparison between our approach and other approaches. Experimental results have shown our approach produces the higher classification accuracy for classification problems and the lower mean squared errors for regression problems, and possesses the better stability.

一、 緒論 1
1.1研究背景與動機 1
1.2研究目的 1
1.3論文架構 2
二、 文獻探討 3
2.1 極限學習機 3
2.2改善極限學習機的解釋性 3
2.3改善極限學習機的準確度 6
2.4處理即時的資料 7
2.5處理不平衡的資料 7
三、 研究方法 9
3.1啟動強度計算改良 10
3.2輸出矩陣計算改良 10
3.3輸出權重計算改良 10
3.4加入遞迴計算 12
四、 實驗結果 14
4.1資料集說明 14
4.2分類問題實驗結果 19
4.3迴歸問題實驗 22
4.4時變問題實驗 25
五、 結論 28
六、 參考文獻 29

[1]Guang-Bin Huang, Qin-Yu Zhu, and Chee-Kheong Siew, “Extreme Learning Machine: A New Learning Scheme of Feedforward Neural Networks.” IEEE International Joint Conference, vol. 2, pp. 985-990, 2004.
[2]Shen Yuong Wong, Keem Siah Yap, Hwa Jen Yap, Shing Chiang Tan, and Siow Wee Chang, “On Equivalence of FIS and ELM for Interpretable Rule-Based Knowledge Representation.” IEEE Transactions on Neural Networks ond Learning Systems, vol. 26, no. 7, pp. 1417-1430, 2015.
[3]Wang, Y. G., Cao, F. L., & Yuan, Y. B. “A study on effectiveness of extreme learning machine.” Neurocomputing, vol. 74, no. 16, pp. 2483–2490, 2011.
[4]Chen, Z. X. X., Zhu, H. Y. Y., & Wang, Y. G. G. “A modified extreme learning machine with sigmoidal activation functions.” Neural Computing & Applications, vol. 22, no. 3, pp. 541–550, 2013.
[5]Huang, G.-B., Chen, L., & Siew, C.-K. “Universal approximation using incremental constructive feedforward networks with random hidden nodes.” IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 879–892. 2006.
[6]Yang, Y. M., Wang, Y. N., & Yuan, X. F. “Bidirectional extreme learning machine for regression problem and its learning effectiveness.” IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 9, pp. 1498–1505. 2012.
[7]Rong, H.-J., Ong, Y.-S., Tan, A.-H., & Zhu, Z. “A fast pruned-extreme learning machine for classification problem.” Neurocomputing, vol. 72, no. 1, pp. 359–366. 2008.
[8]Zhang, R., Lan, Y., Huang, G. B., & Xu, Z. B. “Universal approximation of extreme learning machine with adaptive growth of hidden nodes.” IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 2, pp. 365–371. 2012
[9]Liang, N.-Y., Huang, G.-B., Saratchandran, P., & Sundararajan, N. “A fast and accurate online sequential learning algorithm for feedforward networks.” IEEE Transactions on Neural Networks, vol. 17, no. 6, pp. 1411–1423. 2006.
[10]Zhao, J. W., Wang, Z. H., & Park, D. S. “Online sequential extreme learning machine with forgetting mechanism.” Neurocomputing, vol. 87, pp. 79–89. 2012.
[11]Zong, W. W., Huang, G.-B.,&Chen, Y. Q. “Weighted extreme learning machine for imbalance learning.” Neurocomputing, vol. 101, pp. 229–242. 2013.
[12]Shie-Jue Lee, Member, IEEE, and Chen-Sen Ouyang, “A Neuro-Fuzzy System Modeling With Self-Constructing Rule Generation and Hybrid SVD-Based Learning.” IEEE Transactions on Fuzzy Systems, vol. 11, no. 3, pp. 341-353, 2003.
[13]Chen-Sen Ouyang, NaiJing Kang, Po-Jen Cheng, “A Recursive SVD-based Least Squares Algorithm With Forgetting Factors for Neuro-Fuzzy Modeling.” IEEE, pp. 575-580, 2013.
[14]Dr. WIlliam H. Wolberg. (1992). UCI Breast Cancer Wisconsin (Original) Data Set [https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Original)]. University of Wisconsin Hospitals.
[15]Ashwin Srinivasan. (1993). UCI Statlog (Landsat Satellite) Data Set [https://archive.ics.uci.edu/ml/datasets/Statlog+(Landsat+Satellite)]. University of Strathclyde, Department of Statistics and Data Modeling.
[16]I-Cheng Yeh. (2007). UCI Concrete Compressive Strength Data Set [https://archive.ics.uci.edu/ml/datasets/Concrete+Compressive+Strength]. Chung-Hua University, Department of Information Management.
[17]Paulo Cortez. (2009). UCI Wine Quality Data Set [https://archive.ics.uci.edu/ml/datasets/Wine+Quality]. University of Minho.

QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關期刊