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1Report
Relation: 自动化学报; http://ir.ia.ac.cn/handle/173211/55724
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2Academic Journal
مصطلحات موضوعية: 软计算, 神经网络, 中医诊断, 中医“八纲辨证”知识库, Soft Compute, NN, TCMD(Traditional Chinese-Medical Diagnosis), TCM Eight Principal Syndromes NN Knowledge Base
Relation: 计算机应用研究,2006,(06):194-195,204; JSYJ200606061; http://dspace.xmu.edu.cn/handle/2288/156537
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3Academic Journal
المساهمون: 北京大学国家视觉与听觉信息处理实验室
المصدر: 知网
Relation: 国际学术动态.1996,(01),28-31+17.; 1045734; http://hdl.handle.net/20.500.11897/183461
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4Report
المؤلفون: 陈求稳, Arthur Mynett, 王菲
Relation: 生态学报; 陈求稳;Arthur Mynett;王菲;.软计算在生态模型中的应用,生态学报,2006,1(8):2594-2601; http://ir.rcees.ac.cn/handle/311016/10962
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5Report
المصدر: 汪定伟,容启亮,叶伟雄. 企业动态结盟中的伙伴挑选模型及其软计算方法[J]. 中国科学E辑,2002,32(6):824-830.
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6Dissertation/ Thesis
المؤلفون: 江增堂
المساهمون: 吳柏林, Wu, Berlin
مصطلحات موضوعية: 非線性, 區間軟計算, 模糊分析, 基因演算法, 門檻自迴歸, 門檻區間, nonlinear, soft computing, fuzzy analysis, genetic algorithms, SETAR, threshold interval
Relation: 吳柏林(1995)，時間數列分析導論，華泰書局，台北。\n吳柏林(2005)，模糊統計導論方法與應用，五南出版社，台北。\n吳柏林、阮亨中(2000)，模糊數學與統計應用，俊傑書局，台北。\n吳柏林、林玉鈞(2002)，模糊時間數列分析與預測-以台灣地區加權股價指數為例，應用數學學報，第25卷，第1期，頁67-76。\n程友梅(1995)，轉移型時間序列的認定。國立政治大學統計系碩士論文。\n張新發(1996)，遺傳演算法在門檻自迴歸模式(d,r)值估計的應用。國立政治大學統計系碩士論文。\n楊亦農(2009)，時間序列分析：經濟與財務上之應用，雙葉書廊，台北。\nF.-M. Tseng and G.-H. Tzeng (2002) a fuzzy seasonal ARIMA model for forecasting. Fuzzy sets and systems, 126(3), 367-376.\nH. T. Nguyen and B. Wu (2006) Fundamentals of Statistics with Fuzzy Data. New York：Springer.\nHansen, B.E. (1997). Inference in TAR Models, Studies in Nonlinear Dynamics and Econometrics, 2, 1-14.\nHsu, H.L. (2008). Evaluating forecasting performance for interval data. Computers and Mathematics with Applications 56, 2155-2163.\nHsu, H. L. (2011). Interval Time Series Analysis with Forecasting Efficiency Evaluation, Doctorial Thesis, Department of Mathematical Science, National Chengchi University, Taipei, Taiwan. \nKumar, K. and Wu, B. (2001).Detection of change points in time series analysis with fuzzy statistics, International Journal of Systems Science 32(9), 1185-1192.\nLudermir, T. B. (2008). Forecasting models for interval-valued time series. Neurocomputing 71, 3228-3238.\nM. Bleaney, N. Gemmell, R.Kneller(1989) Testing the endogenous growth model: public expenditure, taxation, and growth over the long run.\nM. Khashei, S.R. Hejazi and M. Bijari (2008) A new hybrid artificial neural networks and fuzzy regression model for time series forecasting. Fuzzy sets and systems, 159, 769-786.\nS.K. Chang (2007) On the Testing Hypotheses of Mean and Variance for Interval Data. Management Science & Statistical Decision, 4(2), 63-69.\nTong, H. & Lim, K. S. (1980). Journal of the Royal Statistical Society, Series B,"Threshold Autoregression, Limit Cycles and Cyclical Data (with discussion)", 42, 245-292.\nTong, H. (1990). Non-Linear Time Series: A Dynamical System Approach. Oxford University Press.\nTseng, F.M., Tseng, G.H., Yu, H.C., and Yuan, B.C. (2001). Fuzzy ARIMA model for forecasting the foreign exchange market. Fuzzy sets and systems 118(1), 9-19.\nV. Kreinovich , H. T. Nguyen and B. Wu (2007) On-line algorithms for computing mean and variance of interval data, and their use in intelligent systems. Information Sciences, 177, 3228-3238.\nWu, B and Hung, S. (1999). Fuzzy Sets and Systems. A fuzzy identification procedure for nonlinear time series with example on ARCH and bilinear models. 108, 275-287.\nWu, B. (2011). Efficiency Evaluation in Time Management for School Administration with Fuzzy Data, Technical Report, Department of Mathematical Science, National Chengchi University, Taipei, Taiwan.\nZhou H. D. (2005). Nonlinearity or structural break – data mining in evolving financial data sets from a Bayesian model combination perspective. Proceedings of the 38th Hawaii International Conference on System Sciences, Hawaii, U.S.A.; G0099751010; https://nccur.lib.nccu.edu.tw//handle/140.119/57061; https://nccur.lib.nccu.edu.tw/bitstream/140.119/57061/-1/101001.pdf
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7Dissertation/ Thesis
المؤلفون: 廖育琳
المساهمون: 吳柏林
Time: 24
وصف الملف: 1428441 bytes; application/pdf
Relation: 中文部分： [1]. 交通部觀光局 (2002)。觀光客倍增計畫。 [2]. 交通部觀光局 (2002)。觀光政策白皮書。 [3]. 交通部觀光局 (2007)。旅遊台灣年計畫。 [4]. 交通部觀光局 (2010)。中華民國年觀光年報。 [5]. 吳柏林 (1995) 時間數列分析與導論。台北：華泰書局。 [6]. 吳柏林、張建瑋 (1996)。非線性時間數列的分類與預測。第三屆三 軍官校基礎學術研討會論文集 98-214。 [7]. 吳柏林 (2000)。模糊數學與統計應用。台北：俊傑書局。 [8]. 吳柏林 (2005)。模糊統計導論:方法與應用。台北：五南出版社。 [9]. 沈中華 (2000)。40分鐘學會匯率危機預測。台北：新陸書局。 [10]. 李榮謙 (1999)。國際貨幣與金融。台北：智勝文化。 [11]. 阮正治 (1996)。遺傳演算法在非線性時間數列結構改變之分析與應用。國立政治大學統計系碩士論文。 [12]. 林茂文 (1992)。時間序列分析與預測。台北：華泰書局。 [13]. 林原宏 (2006)。模糊統計。台北：五南出版社。 [14]. 程友梅 (1995)。轉折型時間序列的認定。國立政治大學統計系碩士論文。 [15]. 張新發 (1996)。遺傳演算法在門檻自迴歸模式(d,r)值估計的應用。國立政治大學統計系碩士論文。 [16]. 楊奕農 (2006)。時間序列分析-經濟與財務上之應用。台北：雙葉書廊。 [17]. 賈昭南 (2002)。國際金融實務與理論。台北：華泰文化。 英文部分： [1]. Chang, S.K. (2007). On the Testing Hypotheses of Mean and Variance for Interval Data. Management Science and Statistical Decision 4(2), 63-69. [2]. Chatfield, C. (1993). Calculating Interval Forecasts. Journal and Business & Economic Statistics 11(2), 121-135. [3]. Chen, S.M. (1996). Forecasting enrollments based on fuzzy time series. Fuzzy sets and systems 81, 311-319. [4]. Huarng, K. (2001). Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy sets and systems 123(3), 387-394. [5]. Hsu, H.L. (2008). Evaluating forecasting performance for interval data. Computers and Mathematics with Applications 56, 2155-2163. [6]. Kashia, M., Hejaz, S.R. and Bijari, M. (2008). A new hybrid artificial neural networks and fuzzy regression model for time series forecasting. Fuzzy sets and systems 159, 769-786. [7]. Kreinovich, V., Nguyen, H.T. and Wu, B. (2007). On-line algorithms for computing mean and variance of interval data, and their use in intelligent systems. Information Sciences 177, 3228-3238. [8]. Ludermir, T.B. (2008). Forecasting models for interval-valued time series. Neurocomputing 71, 3344-3352. [9]. Nguyen, H.T. and Wu, B. (2006). Fundamentals of Statistics with Fuzzy Data. New York：Springer. [10]. Römer, C. and Kandel, A. (2000). Statistical tests for fuzzy data. Fuzzy sets and systems 72(1), 1-26. [11]. Tong, R.M. (1978). Synthesis of fuzzy models for industrial processes. International Journal of General Systems 5(4), 143-162. [12]. Tsay, R.S. (1991). Detecting and modeling non-linearity in univariate time series Analysis. Statistica Sinica 1(2), 431-451. [13]. Tseng, F.M., Tseng, G.H., Yu, H.C., and Yuan, B.C. (2001). Fuzzy ARIMA model for forecasting the foreign exchange market. Fuzzy sets and systems 118(1),9-19. [14]. Tseng, F.M. and Tseng, G.H. (2002). A fuzzy seasonal ARIMA model for forecasting. Fuzzy sets and systems 126(3), 367-376.; G0096751004; http://nccur.lib.nccu.edu.tw//handle/140.119/60075
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8Dissertation/ Thesis
المؤلفون: 施明辉
المساهمون: 周昌乐
مصطلحات موضوعية: 中医辨证, 软计算, 知识发现, 属性约简, 约简分辨图, Syndrome Differentiation in Traditional Chinese Medicine, Soft Computing, Knowledge Discovery, Attribute Reduction, Reduct Discernibility Graph
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9Dissertation/ Thesis
المؤلفون: 吴芸
المساهمون: 周昌乐
مصطلحات موضوعية: 中医辨证信息处理, 辨证计算方法, 神经网络, 遗传算法, 软计算融合方法, Information Process for Traditional Chinese Medicine (TCM), Computation of Syndromes Differentitation, NN, Genetic Algorithm, Combinational Soft Computing
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10
المؤلفون: 李佳宁
المساهمون: 易建强
مصطلحات موضوعية: 移动机器人, 自治导航, 软计算, 神经模糊推理网络, 强化学习, Mobile Robot, Autonomous Navigation, Soft Computing
Relation: http://ir.ia.ac.cn/handle/173211/5844
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11
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12Dissertation/ Thesis
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13
المؤلفون: 任剑
المساهمون: 郑应平
مصطلحات موضوعية: 模糊混杂控制系统, 混杂控制系统, 软计算, 模糊逻辑, 神经网络, 遗传算法, 模糊神经网络, Fuzzy Hybrid Control System, Hybrid Control System, Soft Computing Fuzzy Logic, Neural Network, Genetic Algorithm, Fuzzy-neural Net
Relation: http://ir.ia.ac.cn/handle/173211/5681