|  | 
						
							| 
									
										|  | 
												
													| Çѱ¹¼öÀÚ¿øÇÐȸ / v.32, no.6, 1999³â, pp.731-740   |  
													| ÀÚ±â»ó°üÇÔ¼öÀÇ ºñ¼±Çü À¯Ãß ÇØ¼® ( Nonlinear Analog of Autocorrelation Function )
 |  
													| ±èÇü¼ö;À±¿ë³²; ¼±¹®´ëÇб³ °Ç¼³°øÇкÎ;°í·Á´ëÇб³ Åä¸ñȯ°æ°øÇаú; |  |  |  
							|  |  
							| 
									
										|  | 
												
													|  |  
													| ÃÊ ·Ï |  
													| ÀÚ±â»ó°üÇÔ¼ö´Â ¼ö¹®½Ã°è¿ÀÇ ¼±Çü»ó°ü °ü°è¸¦ ³ªÅ¸³»´Â ôµµ·Ñ ³Î¸® ÀÌ¿ëµÇ°í ÀÖ´Ù. ±×·¯³ª ºñ¼±Çü µ¿¿ªÇп¡¼ ÇʼöÀûÀÎ Áöü½Ã°£ ¶Ç´Â ¹«»ó°ü½Ã°£ $	au$d¸¦ »êÁ¤Çϴµ¥´Â ÀûÇÕÇÏÁö ¾ÊÀ»¼öµµ Àֱ⠶§¹®¿¡ ºñ¼±Çü »ó°ü°ü°èÀÇ Ã´µµ·Î »óÈ£Á¤º¸ÀÌ·ÐÀÌ ÃßõµÇ¾î ¿Ô´Ù. ÃÖ±Ù¿¡ ÀϺΠÇÐÀÚµéÀº Ä«¿À½º µ¿¿ªÇÐ ºÐ¼®À» À§ÇÏ¿© Áöü½Å°£ $	au$d´ë½Å¿¡ »óÅ °ø°£»ó¿¡ ±¸ÃàµÈ °¢ »óÅ º¤Å¸Á¡ ¼ººÐµéÀÇ Ãѽð£À» Ç¥½ÃÇÏ´Â Áöü½Ã°£Ã¢À» Á¦¾ÈÇÏ¿´´Ù. ±×·¯³ª Áöü½Å°£Ã¢Àº ÀÚ±â»ó°üÇÔ¼ö³ª »óÈ£Á¤º¸À̷п¡ ÀÇÇØ ÃßÁ¤µÉ ¼ö ¾ø´Ù. ±âº»ÀûÀ¸·Î Áöü½Å°£Ã¢Àº ½Ã°è¿ ÀÚ·áÀÇ »ó°ü°ü°è°¡ °¡Àå ÀÛÀ» ÃÖÀû½Ã°£À̸ç Áöü½Ã°£Àº ±¹ÁöÀûÀÎ ÃÖ¼Ò°ª Áß Ã¹ ¹øÂ°ÀÇ ÃÖÀû½Ã°£ÀÌ´Ù. º» ¿¬±¸¿¡¼´Â ¼ö¹®½Ã°è¿ÀÇ Áöü½Ã°£°ú Áöü»ç°£Ã¢À» ±¸Çϱâ À§ÇÏ¿© C-C¹ä¹ýÀ̶ó´Â ±â¹ýÀ» ÀÌ¿ëÇϰí, ¿©±â¿¡¼ »êÁ¤µÈ °ªµéÀ» ±Ù°Å·Î ¼ö¹®½Ã°è¿ÀÇ ¸ðÇüÈ¿Í ¿¹Ãø¿¡ Áß¿äÇÑ ¼±Çü ¶Ç´Â ºñ¼±Çü Á¾¼Ó¼ºÀ» ÆÄ¾ÇÇϰíÀÚ ÇÑ´Ù. |  
													|  |  
													| Autocorrelation function is widely used as a tool measuring linear dependence of hydrologic time series. However, it may not be appropriate for choosing decorrelation time or delay time ${	au}_d$ which is essential in nonlinear dynamics domain and the mutual information have recommended for measuring nonlinear dependence of time series. Furthermore, some researchers have suggested that one should not choose a fixed delay time ${	au}_d$ but, rather, one should choose an appropriate value for the delay time window ${	au}_d={	au}(m-1)$, which is the total time spanned by the components of each embedded point for the analysis of chaotic dynamics. Unfortunately, the delay time window cannot be estimated using the autocorrelation function or the mutual information. Basically, the delay time window is the optimal time for independence of time series and the delay time is the first locally optimal time. In this study, we estimate general dependence of hydrologic time series using the C-C method which can estimate both the delay time and the delay time window and the results may give us whether hydrologic time series depends on its linear or nonlinear characteristics which are very important for modeling and forecasting of underlying system. |  
													|  |  
													| Ű¿öµå |  
													| ÀÚ±â»ó°üÇÔ¼ö;»ó°üÀûºÐ;Áöü½Ã°£;Áöü½Ã°£Ã¢;autocorrelation function;correlation integral;delay time window; |  
													|  |  |  |  
							|  |  
							| 
									
										|  | 
												
													| Çѱ¹¼öÀÚ¿øÇÐȸ³í¹®Áý / v.32, no.6, 1999³â, pp.731-740 Çѱ¹¼öÀÚ¿øÇÐȸ
 ISSN : 1226-6280
 UCI : G100:I100-KOI(KISTI1.1003/JNL.JAKO199911920063436)
 ¾ð¾î : ¿µ¾î
 |  
													|  |  
													| ³í¹® Á¦°ø : KISTI Çѱ¹°úÇбâ¼úÁ¤º¸¿¬±¸¿ø |  
													|  |  |  |  
							|  |  |