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論文中文名稱:最佳化廣義迴歸類神經網路於預測模型之研究 [以論文名稱查詢館藏系統]
論文英文名稱:The Study of Predictive Models Based on the Optimal General Regression Neural Networks [以論文名稱查詢館藏系統]
院校名稱:臺北科技大學
學院名稱:機電學院
系所名稱:自動化科技研究所
畢業學年度:99
出版年度:100
中文姓名:邵時俊
英文姓名:Shih-Chun Shao
研究生學號:98618007
學位類別:碩士
語文別:中文
口試日期:2011-07-28
論文頁數:85
指導教授中文名:陳文輝
口試委員中文名:陳俊宏;楊文治;宋國明
中文關鍵詞:預測模型廣義迴歸類神經網路最佳化演算法資料前處理錯誤隱藏
英文關鍵詞:Predictive ModelGeneral Regression Neural NetworksOptimal AlgorithmsData Pre-processingError Concealment
論文中文摘要:在許多的工程應用中皆會利用預測模型來解決實際上的問題,因此如何根據實際工程應用上所累積的歷史數據資料,來建構一個客觀且具有高精確性的預測模型為本論文研究之重點。本論文使用廣義迴歸類神經網路作為預測模型之核心演算法,廣義迴歸類神經網路學習速度快,僅需決定平滑參數即可決定整體網路之性能,利用少量的歷史數據資料便可達到高度的預測精準性。最後,本論文針對平滑參數利用交叉驗證法、基因演算法以及粒子群演算法去計算出最佳平滑參數,進而分別建立三種不同的預測模型,如此便能確保本論文所提出之預測模型有著最好的預測推論能力。
本論文首先驗證並分析此三種預測模型的預測推論能力,再根據預測模型之特性,分別針對兩種不同的研究案例做應用;第一個研究案例為資訊末端設備之資料校正前處理應用,第二個研究案例為H.264/AVC視頻通訊之錯誤隱藏。在第一個研究案例中,本論文將利用所提出之預測模型與傳統模糊演算法、倒傳遞類神經網路做誤差校正能力的分析比較;在第二個研究案例中,本論文將所提出之預測模型加入空間域錯誤隱藏演算法,改善傳統僅使用時間域錯誤隱藏演算法的缺點。由上述兩種不同研究案例之模擬實驗結果顯示,本論文所提出之預測模型無論輸入資料為何種型態,皆能有效地解決問題,亦有較高的實際工程應用價值。
論文英文摘要:In engineering applications, the predictive models are always adopted to solve the actual problems. Therefore, the aim of this thesis is to study how to build up a high accuracy predictive model according to the historical data in engineering applications. Hence, general regression neural networks are applied as the core algorithm of predictive models in this thesis. It is because after we choose the spread constant, the features of the whole general regression neural networks can be determined. Therefore, it has a higher-speed learning ability than other neural networks. Also, it achieves the high accuracy of prediction with few historical sample data. Finally, this thesis adopts cross-validation method, genetic algorithms as well as particle swarm optimization to find out the best spread constant to build up three different predictive models so as to make sure they have the best prediction inferences in this thesis.
First, this thesis will examine and analyze the prediction inferences of these three predictive models. Then, they will be applied to two different cases according to their features. One is the application of the data pre-processing of remote terminal units, and the other is the H.264/AVC error concealment in video communication. In the first case, this thesis will apply the proposed predictive models compared with fuzzy algorithms and back-propagation neural networks to conduct the analyzing comparison of error calibrating. In the second case, the algorithm of spatial error concealment will be added into the predictive model to improve the shortcomings of merely adopting traditional temporal error concealment. According to the experimental results of two cases, the predictive models in this thesis can solve the problems effectively no matter which type of data is imported. Furthermore, they also have a high valuation in engineering applications.
論文目次:摘要 i
中文摘要 ii
誌謝 iv
目錄 v
表目錄 vii
圖目錄 viii
第一章 緒論 1
1.1 預測模型 1
1.2 研究動機及目的 1
1.3 論文架構 2
第二章 廣義迴歸類神經網路 4
2.1 簡述廣義迴歸類神經網路 4
2.2 推導廣義迴歸類神經網路數學模型 5
2.3 說明廣義迴歸類神經網路之訓練學習過程 6
第三章 最佳化演算法 9
3.1 前言 9
3.2 交叉驗證法 10
3.3 基因演算法 11
3.4 粒子群演算法 16
3.5 平滑參數最佳化 18
第四章 資訊末端設備之資料前處理應用 25
4.1 前言 25
4.2 電力遠端控制與資料擷取系統 26
4.3 資訊末端設備 27
4.4 研究案例描述 30
4.5 相關文獻回顧 32
4.6 模擬實驗與結果討論 35
第五章 H.264/AVC視頻通訊之錯誤隱藏 43
5.1 前言 43
5.2 H.264/AVC 標準 44
5.3 研究案例描述 46
5.4 相關文獻回顧 47
5.4.1 時間域錯誤隱藏演算法 47
5.4.2 空間域錯誤隱藏演算法 53
5.4.3 時間域結合空間域錯誤隱藏演算法 55
5.5 預測模型應用於錯誤隱藏 60
5.6 模擬實驗與結果討論 62
第六章 結論與未來展望 80
6.1 結論 80
6.2 未來展望 80
參考文獻 81
作者簡介 85
論文參考文獻:[1] 許哲強,台灣區域電力負載預測分析系統之建立與應用研究,博士論文,國立成功大學,資源工程學系碩博士班,台南,2002。
[2] 雷德仁,國防預算預測之研究:應用類神經網路與支援向量機,碩士論文,國防管理學院,國防財務資源研究所,新北市,2006。
[3] 李正忠,應用預測組合模式改進交通隧道 空氣品質預測之研究,博士論文,國立雲林科技大學,工程科技研究所,雲林,2007。
[4] 李穎,類神經網路應用於國道客運班車旅行時間預測模式之研究,博士論文,國立成功大學,交通管理學系碩博士班,台南,2002。
[5] J. H. Chen, S. C. Shao, and W. H. Chen, “Improving Temporal Error Concealment by GRNN in Video Communication,” IEEE International Conference on Multimedia and Expo, July11-15, 2011.
[6] G. Rigatos, P. Siano, and A. Piccolo, “Neural Network-Based Approach For Early Detection of Cascading Events in Electric Power Systems,” IET Generation on Transmission & Distribution, vol. 3, no. 7, July 2009, pp. 650-665.
[7] D. F. Specht, “Probabilistic Neural Networks for Classification, Mapping, or Associative Memory,” IEEE International Conference on Neural Networks, 24-27 July 1988.
[8] D. F. Specht, “A General Regression Neural Network,” IEEE Transactions on Neural Networks, vol. 2, no. 6, Nov. 1991, pp. 568-576.
[9] 羅華強,類神經網路-MATLAB的應用,台北,高立圖書有限公司,2005年第七版。
[10] E. Parzen, “On Estimation of a Probability Density Function and Mode,” Annals Of Mathematical Statistics, vol. 33, no. 3, Sep. 1962, pp. 1065-1076.
[11] T. P. Patalia, and G. R. Kulkarni, “Behavioral Analysis of Genetic Algorithm for Function Optimization,” IEEE International Conference on Computational Intelligence and Computing Research, 28-29 Dec. 2010.
[12] del Valle, Y. Venayagamoorthy, G. K. Mohagheghi, S. Hernandez, J.-C. Harley, and R. G. , “Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems,“ IEEE Transactions on Evolutionary Computation, vol. 12, no. 2, Apr. 2008, pp. 171-195.
[13] J. G. Carney, P. Cunningham, “Tuning Diversity in Bagged Ensenbles,” International Journal of Neural System, vol. 10, no. 4, May 2000, pp. 267-279.
[14] Y. Liu, “Create Stable Neural Networks by Cross-Validation,” IEEE International Joint Conference on Neural Networks, 16-21 July 2006, pp. 3925.
[15] J. H. Holland, Adaptation in Natural and Artificial Systems, AnnArbor: The University of Michigan Press, 1975.
[16] 呂怡廷,多目標基因演算法於鋼筋混凝土結構設計之應用,碩士論文,國立交通大學,土木工程所,新竹,2008。
[17] 田川昇,利用基因演算法與類神經網路建立台灣西南海域深部地層滲透率模式之研究,碩士論文,國立成功大學,資源工程學系碩博士班,台南,2002。
[18] J. Kennedy, and R. Eberhart, “Particle Swarm Optimization,” IEEE International Conference on Neural Networks, vol. 4, 27 Nov.-1 Dec. 1995, pp. 1942-1948.
[19] D. Karaboga, “An Idea Based on Honey Bee Swarm for Numerical Optimization,” Technical Report TR06, Computer Engineering Department, Erciyes University, Turkey, 2005.
[20] C. Blum, “Ant Colony Optimization: Introduction and Recent Trends,” Physics of Life Reviews, Oct. 2005, pp. 353-373.
[21] T. J. Lui, W. Stirling, and H. O. Marcy, “Get Smart,“ IEEE Power and Energy Magazine, vol. 8, no. 3, May-June 2010, pp. 66-78.
[22] M. S. Thomas, P. Kumar, and V. K. Chandna, “Design, Development, and Commissioning of A Supervisory Control and Data Acquisition (SCADA) Laboratory for Research and Training,” IEEE Transactions on Power Systems, vol. 19, no. 3, Aug. 2004, pp. 1582-1588.
[23] H. L. Smith, and W. R. Block, “Rtus Slave for Supervisory Systems [Power Systems],” IEEE Computer Applications in Power, vol. 6, no. 1, Jan. 1993, pp. 27-32,.
[24] 陳大業,變電所監控裝置即時告警之研究,碩士論文,國立台北科技大學,電機工程研究所,台北,2009。
[25] 黃銘宏,配電調度與公路點滅控制-應用於台電系統,碩士論文,國立中正大學,電機工程研究所,嘉義,2004。
[26] P. Kumar, V. K. Chandna, and M. S. Thomas, “Intelligent Algorithm for Preprocessing Multiple Data at RTU,” IEEE Transactions on Power Systems, vol. 18, no. 4, Nov. 2003, pp. 1566-1572.
[27] P. Kumar, V. K. Chandna, and M. S. Thomas, “Fuzzy-Genetic Algorithm for Pre-Processing Data at the RTU,” IEEE Transactions on Power Systems, vol. 19, no. 2, May 2004, pp. 718-723.
[28] A. A. Najafi, and R. M. T. Akbarzadeh, “Design a Real-Time Preprocessing Algorithm Based on A Co-Evolutionary Fuzzy System,” IEEE Intelligent Computer Communication and Processing, 6-8 Sep. 2007, pp. 297-303.
[29] “Coding of Moving Pictures and ASSOCIATED Audio for Digital storage Media at up About 1.5Mbits/s,” ISO/IEC 1117-2: Video (MPEG-1), Nov 1991.
[30] “Generic Coding of Moving Pictures and ASSOCIATED Audio Information,” ISO/IEC13818-2: Video (MPEG-2), May 1996.
[31] “Coding of Audio-Visual Objects-Part 2: Visual,” ISO/IEC 14496-2 (MPEG-4 Visual Version 1), Apr 1999.
[32] Joint Video Team, “Draft ITU-T Recommendation and Final Draft International Standard of Joint Video Specification,” ITU-T Recommendation H.264 and ISO/IEC 14496-10 AVC, 2003
[33] A. Vetro, T. Wiegand, and G. J. Sullivan, “Overview of the Stereo and Multiview Video Coding Extensions of the H.264/MPEG-4 AVC Standard,” IEEE Proceedings, vol. 99, no. 4, Apr. 2011, pp. 626-642.
[34] 王永富,H.264即時快速編碼法則之研究,碩士論文,國立中央大學,通訊工程研究所,桃園,2008。
[35] W. M. Lam, A. R. Reibman, and B. Liu, “Recovery of Lost or Erroneously Received Motion Vectors,” IEEE International Conference on Acoustics, Speech, and Signal Processing, 27-30 Apr. 1993.
[36] C. Y. Su, and C. H. Huang, “Temporal Error Concealment Algorithm Using Multi-Side Boundary Matching Principle,” IEEE International Symposium on Signal Processing and Information Technology, Aug. 2006, pp. 448-453.
[37] M. C. Hwang, J. H. Kim, D.T. Dung, and S. J. Ko, “Hybrid Temporal Error Concealment Methods for Block-Based Compressed Video Transmission,” IEEE Transactions on Broadcasting, vol. 54, no. 2, June 2008, pp. 198-207.
[38] Y. Wang, and Q. F. Zhu, “Error Control and Concealment for Video Communication: A Review,” IEEE Processing, vol. 86, no. 5, May 1998, pp. 974-997.
[39] J. Zheng, and L. P. Chau, “A Motion Vector Recovery Algorithm for Digital Video Using Lagrange Interpolation,” IEEE Transactions on Broadcasting, vol. 49, no. 4, Dec. 2003, pp. 383-389.
[40] J. Zhou, B. Yan, and H.Gharavi, “Efficient Motion Vector Interpolation for Error Concealment of H.264/AVC,” IEEE Transactions on Broadcasting, vol. 57, no. 1, Nov. 2010, pp. 75-80.
[41] Q. Peng, T. Yang, and C. Zhu, “Block-Based Temporal Error Concealment for Video Packet Using Motion Vector Extrapolation,” IEEE International Conference on Communications, Circuits and Systems and West Sino Expositions, 29 June-1 July 2002.


[42] J. W. Suh, and Y. S. Ho, “Error Concealment Based on Directional Interpolation,” IEEE Transactions on Consumer Electronics, vol. 43, no. 3, Aug. 1997, pp. 295-302.
[43] W. Kim, J. Koo, and J. Jeong, “Fine Directional Interpolation for Spatial Error Concealment,” IEEE Transactions on Consumer Electronics, vol. 52, no. 3, Aug. 2006, pp. 1050-1056.
[44] M. Kim, H. Lee, and S. Sull, “Spatial Error Concealment for H.264 Using Sequential Directional Interpolation,” IEEE Transactions on Consumer Electronics, vol. 54, no. 4, Nov. 2008, pp. 1811-1818.
[45] J. Zhang, F. Liu, H. Shao, and G. Wang, “An Effective Error Concealment For H.264 Decoder Based on Video Scene Change Detection,” Fourth International Conference on Image and Graphics, 22-24 Aug. 2007.
[46] N. Cao, and Z. Li, “An Effective Error Concealment Method Based on the Scene Change,” International Congress on Image and Signal Processing, 17-19 Oct. 2009.
[47] S. C. Hsia, S. C. Cheng, and S. W. Chou, “Efficient Adaptive Error Concealment Technique for Video Decoding System,” IEEE Transactions on Multimedia, vol. 7, no. 5, Oct. 2005, pp. 860-868.
[48] E. S. Ng, J. Y. Tham, and S. Rahardja, “Edge Weighted Spatio-Temporal Search for Error Concealment,” IEEE International Conference on Image Processing, vol. 4, 16 Sep.-19 Oct. 2007.
[49] H. Sun, and W. Kwok, “Concealment of Damaged Block Transform Coded Images Using Projections onto Convex Sets,” IEEE Transactions on Image Processing, vol. 4, no. 4, Apr. 1995, pp. 470-477.
[50] Y. K. Wang, M. M. Hannuksela, V. Varsa, A. Hourunranta, and M. Gabbouj, “The Error Concealment Feature in the H.26L Test Model,” International Conference on Image Processing, vol. 2, 2002, pp. 729-732.
[51] V. Varsa, M. M. Hannuksela, and Y. K. Wang, “Nonnormative Error Concealment Algorithms,” ITU-T VCEG-N62, 2001.
論文全文使用權限:同意授權於2013-08-22起公開