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題名 時間數列分析在偵測型態結構差異上之探討
Application Of Time Series Analysis In Pattern Recgnition And alysis作者 蘇曉楓
Su, Shiau Feng貢獻者 吳柏林
Wu, Berlin
蘇曉楓
Su, Shiau Feng關鍵詞 非線性時間數列模式
神經網路
穩健性
模型辨識
時間數列分析
nonlinear time series
neural
time series analysis日期 1993 上傳時間 29-四月-2016 16:43:56 (UTC+8) 摘要 依時間順序出現之一連串觀測值，通常會呈現某一型態，而根據所產生的型態可以作為判斷事件發生的基礎。例如，震波形成原因的判斷﹔追查環境污染源﹔以及在醫學方面，辨識一個正常人心電圖的型態與患有心臟病的病人其心電圖的型態…等。對於這些問題，傳統之辨識方法常因前提假設的限制而失去其準確性。在本文中，我們應用神經網路中的逆向傳播演算法則來訓練網路，並利用此受過訓練的網路來辨別線性時間數列ARIMA及非線性時間數列 BL(1,0,1,1)。結果發現，網路對於模擬資料中雙線性係數介於0.2至$0.8$之間的資料有高達$80\\%$以上的辨識能力。而在實例研究中，我們訓練網路來判斷震波形成的原因，其正確率亦高達80\\%以上。同時，我們也將神經網路應用在環境保護方面，訓練網路來判斷二地區空氣品質的型態。
A series of observations indexed in time often produces a參考文獻 Brockett,R.W. (1976). Volterra series and geometric control theory, A`utomatica ,Vo1.12,167-176. Chin,L. C. (1985). vVhat is biostatistics? ,Biometrics,41 ,771-775. Chan,D.Y.C. & Prager,D.(1991).Analysis of time series by neural networks: IEEE,355-360. De Gooi.jer,J.G.& Kumar,K.(1992).Some recent developments in nonlinear time series lllodelling,testing and forecasting.International Journal of Forecasting ,8,135-156. Farrugia,S., Yee,H.& Nickolls,P.,( 1991) .Neural networks classification of intracardiac ECGS,JEEE~1278-1283. Gedeon,T.D. & Harris,D.(1991).Creating robust networks,JEEE, 2553-2557. Ghosh,J.) Deuser) L. wI. & Beck,S. D. (1992). A nerual network based hybird systen1 for detection, characterization , and classification of short-duration oceanic signals, IEEE Journal Of Oceanic Engineer` lng ,Vo1.17 No.4 October , 351-363. Gorman,R.P. & Seinowski,T.J. (1988).Analysis of hidden units 1Il a layered netwok trained to classify sonar targets,Neural Networks,Vo1.1,75-89. Granger,C.vV.J.& Anderson)\\.P.(1978).An Introd`uction to Bilinear Ti`me Series M odels,Vandenhoeck and Ruprecht,Gottingent. Granger,C."\\;V.J.(1991).Developments in the nonlinear analysis of econoillic series.Scand.1. of Econo`mics,93(2) ,263-276. Guegan,D & Phalll,T.D.(1992).Power of the score test against bilinear tilDe series models.Statistica Sinica,Vo1.2))57-169. Kanaya,F.& IVIiyake,S.(1991).Bayes statistical behavior and valid generalization of pattern classifying neural networks, IEEE transactio`f1 on N e`ural lVetworks,Vo1.2,No.4,J uly,4 71-475. Ljung,G.:NI. (1978). On a lneasure of lack of fit In tilne senes l1lodels Bio`me tries, Vol. 65,297 -303. Lipplllann,R .P. (1989). Pattern classification uSIng neural net- works IEEE Corn;munication Magazine. Mohler ,R. R. (1973). Bilinear control processes ,Acaclenlic Press, New Yorkand London. Robert,J.S.(1992).Pattern Recognition. Ruberti,A.,Isidori A. & d`Allessanclro,P.(1972). Theory of bilinear dynam,ical system,Springer Verlag,Berlin. Shulllway,R.H.(1988).Applied Statistical Ti`me Series Ana.lysis. Shibata,R.(1976). Selection of the order of an autoregressive nlodel by akaike`s infonnation criterion ,Biometrics, Vol.63(1) , 117. Saikkonen,P.& Luukkonen,R.(1988). Lagrange multiplier tests for testing nonlineari ties in time series lllodeis ,S cand J oural of Statistics, 55-68. Saikkonen, P. & Luukkonen,R. (1988).Power properties of a tilne series linearity test against SOllle simpe bilinear alternatives,Statistica Sinica,453-464. Subba Rao,T. & Gabr,NI.NI.(1984).An Introduction to Bispectral Analysis and Bilinear Time Series Nlodels;Springer- Verlag,Berlin. Terence, C.NI. (1992) .NIodelling the seasonal patterns in UI( macroeconomic tilnes series,Journal of Royal Statistical Society ,61-75. Tsay,R.S. (1991) .Detecting and modeling nonlinearity in univariate tinle series analysis,Statistica Sinica,431-451. Takayuki, Y.,Tetsuro, Y.(1992).Neural networks controller USlllg autotun-ing methodfor nonlinear function,IEEE Transcation on N ev:ral JVetworks, 3( 4), 595-601. Tong,H.& Lim,I(.S. (1980).Tlueshold autoregressioIl,limit cycles and cyclical data. l.Roy. Statist. Soc.Ser.B,42)45-292. \\iVeigend,A.S.& Rumelhart,D.E.(1991).The effective dilnention of space of hidden U nits,IEEE,2069-207 4. Yee E. & Ho J.( 1990). Neural nenwork recognition And classification of aecrosol distri butions nleasured with a two-spot laser velocimeter ,A pplied Opiics ,2929-2938. Zaknich,A. & Attikiouzel,Y. (1991) . A 1110dified probabilistic neural network (PNN) for nonlinear ti111e series analysis, IEEE ,1530-1535. 描述 碩士
國立政治大學
統計學系
G80354009資料來源 http://thesis.lib.nccu.edu.tw/record/#B2002004195 資料類型 thesis dc.contributor.advisor 吳柏林 zh_TW dc.contributor.advisor Wu, Berlin en_US dc.contributor.author (作者) 蘇曉楓 zh_TW dc.contributor.author (作者) Su, Shiau Feng en_US dc.creator (作者) 蘇曉楓 zh_TW dc.creator (作者) Su, Shiau Feng en_US dc.date (日期) 1993 en_US dc.date.accessioned 29-四月-2016 16:43:56 (UTC+8) - dc.date.available 29-四月-2016 16:43:56 (UTC+8) - dc.date.issued (上傳時間) 29-四月-2016 16:43:56 (UTC+8) - dc.identifier (其他 識別碼) B2002004195 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/89020 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 統計學系 zh_TW dc.description (描述) G80354009 zh_TW dc.description.abstract (摘要) 依時間順序出現之一連串觀測值，通常會呈現某一型態，而根據所產生的型態可以作為判斷事件發生的基礎。例如，震波形成原因的判斷﹔追查環境污染源﹔以及在醫學方面，辨識一個正常人心電圖的型態與患有心臟病的病人其心電圖的型態…等。對於這些問題，傳統之辨識方法常因前提假設的限制而失去其準確性。在本文中，我們應用神經網路中的逆向傳播演算法則來訓練網路，並利用此受過訓練的網路來辨別線性時間數列ARIMA及非線性時間數列 BL(1,0,1,1)。結果發現，網路對於模擬資料中雙線性係數介於0.2至$0.8$之間的資料有高達$80\\%$以上的辨識能力。而在實例研究中，我們訓練網路來判斷震波形成的原因，其正確率亦高達80\\%以上。同時，我們也將神經網路應用在環境保護方面，訓練網路來判斷二地區空氣品質的型態。 zh_TW dc.description.abstract (摘要) A series of observations indexed in time often produces a en_US dc.description.tableofcontents 壹 前言‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ ３ 貳 型態辨識探討‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ ６ 2.1 型態辨識的方法‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ ７ 2.1.1 靜態資料‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ ７ 2.1.2 動態資料‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ １１ 2.1.3 穩健性的探討‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ １２ 參 神經網路在非線性時間數列模型辨識之應用 3.1 神經網路介紹‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ １４ 3.2 雙線性模式的探討‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ １７ 3.3 應用神經網路做模型辨識‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ ２０ 3.1 模擬比較與結果‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ ２２ 肆 實例研究 例4.1 地震震波與核子試爆震波的辨識‧‧‧‧‧‧‧‧‧‧‧‧‧ ２９ 例4.2 環保污染品質型態的判別‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ ３０ 伍 結論‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ ３４ 參考文獻‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ ３５ zh_TW dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#B2002004195 en_US dc.subject (關鍵詞) 非線性時間數列模式 zh_TW dc.subject (關鍵詞) 神經網路 zh_TW dc.subject (關鍵詞) 穩健性 zh_TW dc.subject (關鍵詞) 模型辨識 zh_TW dc.subject (關鍵詞) 時間數列分析 zh_TW dc.subject (關鍵詞) nonlinear time series en_US dc.subject (關鍵詞) neural en_US dc.subject (關鍵詞) time series analysis en_US dc.title (題名) 時間數列分析在偵測型態結構差異上之探討 zh_TW dc.title (題名) Application Of Time Series Analysis In Pattern Recgnition And alysis en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Brockett,R.W. (1976). Volterra series and geometric control theory, A`utomatica ,Vo1.12,167-176. Chin,L. C. (1985). vVhat is biostatistics? ,Biometrics,41 ,771-775. Chan,D.Y.C. & Prager,D.(1991).Analysis of time series by neural networks: IEEE,355-360. De Gooi.jer,J.G.& Kumar,K.(1992).Some recent developments in nonlinear time series lllodelling,testing and forecasting.International Journal of Forecasting ,8,135-156. Farrugia,S., Yee,H.& Nickolls,P.,( 1991) .Neural networks classification of intracardiac ECGS,JEEE~1278-1283. Gedeon,T.D. & Harris,D.(1991).Creating robust networks,JEEE, 2553-2557. Ghosh,J.) Deuser) L. wI. & Beck,S. D. (1992). A nerual network based hybird systen1 for detection, characterization , and classification of short-duration oceanic signals, IEEE Journal Of Oceanic Engineer` lng ,Vo1.17 No.4 October , 351-363. Gorman,R.P. & Seinowski,T.J. (1988).Analysis of hidden units 1Il a layered netwok trained to classify sonar targets,Neural Networks,Vo1.1,75-89. Granger,C.vV.J.& Anderson)\\.P.(1978).An Introd`uction to Bilinear Ti`me Series M odels,Vandenhoeck and Ruprecht,Gottingent. Granger,C."\\;V.J.(1991).Developments in the nonlinear analysis of econoillic series.Scand.1. of Econo`mics,93(2) ,263-276. Guegan,D & Phalll,T.D.(1992).Power of the score test against bilinear tilDe series models.Statistica Sinica,Vo1.2))57-169. Kanaya,F.& IVIiyake,S.(1991).Bayes statistical behavior and valid generalization of pattern classifying neural networks, IEEE transactio`f1 on N e`ural lVetworks,Vo1.2,No.4,J uly,4 71-475. Ljung,G.:NI. (1978). On a lneasure of lack of fit In tilne senes l1lodels Bio`me tries, Vol. 65,297 -303. Lipplllann,R .P. (1989). Pattern classification uSIng neural net- works IEEE Corn;munication Magazine. Mohler ,R. R. (1973). Bilinear control processes ,Acaclenlic Press, New Yorkand London. Robert,J.S.(1992).Pattern Recognition. Ruberti,A.,Isidori A. & d`Allessanclro,P.(1972). Theory of bilinear dynam,ical system,Springer Verlag,Berlin. Shulllway,R.H.(1988).Applied Statistical Ti`me Series Ana.lysis. Shibata,R.(1976). Selection of the order of an autoregressive nlodel by akaike`s infonnation criterion ,Biometrics, Vol.63(1) , 117. Saikkonen,P.& Luukkonen,R.(1988). Lagrange multiplier tests for testing nonlineari ties in time series lllodeis ,S cand J oural of Statistics, 55-68. Saikkonen, P. & Luukkonen,R. (1988).Power properties of a tilne series linearity test against SOllle simpe bilinear alternatives,Statistica Sinica,453-464. Subba Rao,T. & Gabr,NI.NI.(1984).An Introduction to Bispectral Analysis and Bilinear Time Series Nlodels;Springer- Verlag,Berlin. Terence, C.NI. (1992) .NIodelling the seasonal patterns in UI( macroeconomic tilnes series,Journal of Royal Statistical Society ,61-75. Tsay,R.S. (1991) .Detecting and modeling nonlinearity in univariate tinle series analysis,Statistica Sinica,431-451. Takayuki, Y.,Tetsuro, Y.(1992).Neural networks controller USlllg autotun-ing methodfor nonlinear function,IEEE Transcation on N ev:ral JVetworks, 3( 4), 595-601. Tong,H.& Lim,I(.S. (1980).Tlueshold autoregressioIl,limit cycles and cyclical data. l.Roy. Statist. Soc.Ser.B,42)45-292. \\iVeigend,A.S.& Rumelhart,D.E.(1991).The effective dilnention of space of hidden U nits,IEEE,2069-207 4. Yee E. & Ho J.( 1990). Neural nenwork recognition And classification of aecrosol distri butions nleasured with a two-spot laser velocimeter ,A pplied Opiics ,2929-2938. Zaknich,A. & Attikiouzel,Y. (1991) . A 1110dified probabilistic neural network (PNN) for nonlinear ti111e series analysis, IEEE ,1530-1535. zh_TW
