Âê̾ | A Regression Analysis Method for Nonlinear and Non-Stationary Stochastic Systems Using Modular Structured Neural Network |
Ãø¼Ô | *Yasuo Mitani (Fukuyama University) |
Page | pp. 431 - 432 |
Keyword | regression analysis, stochastic system, local stationary process, neural network |
Abstract | In this report, a modeling method for non-stationary stochastic systems is proposed under the assumption of local stationary process. In order to evaluate reasonably the non-stationary property, we must consider the time varying statistics or changing property of some parameters reflecting the time varying statistics. Here, a regression analysis method for such stochastic systems is proposed by introducing a modular structured neural network. This modular structured neural network is constructed by the hierarchical combination of each neural network for analyzing the regression characteristics between input and output signals in the local stationary section, and a neural network for the prediction of weights contained in each neural network. |
Âê̾ | DVD-RAM¤Ë¤ª¤±¤ëECC¥Õ¥©¡¼¥Þ¥Ã¥È¤ÎǽÎÏɾ²Á |
Ãø¼Ô | *µÈ¼ ÆÁÂÙ (¹Å繩¶ÈÂç³ØÂç³Ø±¡¹©³Ø·Ï¸¦µæ²ÊÅŵ¤ÅŻҹ©³ØÀ칶), ¸ÅÀî µ±Íº (¹Å繩¶ÈÂç³Ø¹©³ØÉôÅŵ¤¥·¥¹¥Æ¥à¹©³Ø²Ê) |
Page | pp. 433 - 434 |
Keyword | DVD-RAM, ¸í¤êÄûÀµ, ECC¥Õ¥©¡¼¥Þ¥Ã¥È, ¥¤¥ó¥¿¡¼¥ê¡¼¥ÖĹ |
Abstract | DVD-RAM¤Ë¤ª¤¤¤Æ¡¤»ØÌæ¤Ë¤è¤ë¸í¤ê¤ËÂФ¹¤ë³Æ¥Õ¥©¡¼¥Þ¥Ã¥È¤Î¸í¤êÄûÀµÇ½ÎϤÎɾ²ÁµÚ¤Ó²þÎɤò¹Ô¤Ã¤¿¡¥¸í¤êÄûÀµ¥Õ¥©¡¼¥Þ¥Ã¥È¤Ë¤Ï¡¤²áµî¤Ë¼ÂÀӤΤ¢¤ë¸í¤êÄûÀµÉä¹æ¤È¤½¤Î²þÎÉÊý¼°¤Ç¤¢¤ë¡¤¡RS-PC¥Õ¥©¡¼¥Þ¥Ã¥È¡¤¢RS-PC¥Õ¥©¡¼¥Þ¥Ã¥È¤Î²þÎÉÊý¼°¡¤£LDC¥Õ¥©¡¼¥Þ¥Ã¥È¡¤¤¥¤¥ì¡¼¥¸¥ãÄûÀµ¥Õ¥©¡¼¥Þ¥Ã¥È¡¤¥¥¤¥ì¡¼¥¸¥ãÄûÀµ¥Õ¥©¡¼¥Þ¥Ã¥È¤Î²þÎÉÊý¼°¤òÍѤ¤¤¿¡¥¼Â¸³¤Ï¡¤¥Ç¥£¥¹¥¯10Ëç(¸í¤ê¤ÎÂ礤µÂç¤È¾®³Æ5Ëç)¤òÍѤ¤¡¤3Âæ¤Î¥É¥é¥¤¥Ö¤Ë¤è¤ê¸í¤êºÆÀ¸¥Ç¡¼¥¿¤ò¼ý½¸¤·¡¤³ÆÄûÀµÉä¹æ½èÍý¥½¥Õ¥È¤Ë¤è¤ê¡¤¸í¤êÄûÀµ½èÍý¸å¤Î»Ä¸¸í¤ê¥Ç¡¼¥¿Î̤ò·×¬¤·¤¿¡¥³Æ¥Õ¥©¡¼¥Þ¥Ã¥È¤ÎÄûÀµÇ½ÎϤϹ⤤½ç¤Ë¥¡¤¢¡¤¡¡¤£¡¤¤¤È¤Ê¤Ã¤¿¡¥ |
Âê̾ | ºÇÂç¶ËȾ·ÂÀ©Ìó¤È½Å¤ß´Ø¿ô¤ò¹Íθ¤·¤¿Äã°èÄ̲áÈùʬ´ï¤ÎÀß·× |
Ãø¼Ô | *Æþ¹¾ Íε¬ (¹ÅçÂç³ØÂç³Ø±¡¹©³Ø¸¦µæ²Ê), ÃæËÜ ¾»Í³, »³ËÜ Æ© (¹ÅçÂç³Ø¹©³Ø¸¦µæ±¡) |
Page | pp. 435 - 436 |
Keyword | IIR¥Õ¥£¥ë¥¿, Äã°èÄ̲áÈùʬ´ï, °ìÈ̲½¤µ¤ì¤¿Ê¬Ê쿹༰¤ÎÀµ¼ÂÀ, À©ÌóÉÕ¤2¼¡·×²èÌäÂê |
Abstract | ËܹƤǤϡ¤Äã°èÄ̲áÈùʬ´ï¤ÎÀß·×Ë¡¤òÄó°Æ¤¹¤ë¡¥¤³¤ì¤ÏÈùʬ¤È¼×ÃǤÎÆÃÀ¤ò·ó¤ÍÈ÷¤¨¤¿½ê°âÉÔ´°Á´Èùʬ¤È¸Æ¤Ð¤ì¤ë¼þÇÈ¿ôÆÃÀ¤òIIR¥Õ¥£¥ë¥¿¤È¤·¤ÆÀ߷פ·¤¿¤â¤Î¤Ç¤¢¤ë¡¥½¾ÍèÉÔ´°Á´Èùʬ¤Ï¡¤Äã°èÄ̲á¥Õ¥£¥ë¥¿¤òÊ»ÍѤ·¤¿¥Ç¥£¥¸¥¿¥ëÈùʬ´ï¤Ç¼Â¸½¤µ¤ì¤ë¼þÇÈ¿ôÆÃÀ¤Ç¤¢¤ê¡¤¥Ç¥£¥¸¥¿¥ëÈùʬ´ï¤Î¼ÂÍѤ˺ݤ·¤ÆÌäÂê¤È¤Ê¤ë¹â¼þÇȥΥ¤¥º¤ò¼×ÃǤ¹¤ëÌÜŪ¤¬¤¢¤ë¡¥¤Þ¤¿¡¤ËܹƤǤϡ¤°ìÈ̲½¤µ¤ì¤¿Ê¬Ê쿹༰¤ÎÀµ¼ÂÀ¤òÄó°Æ¤¹¤ë¡¥¤³¤ì¤Ï¡¤IIR¥Õ¥£¥ë¥¿ÆÃͤÎÀß·×ÌäÂê¤Ç¤¢¤ë¶ËÇÛÃ֤ˤª¤¤¤Æ¡¤¤½¤ÎºÇÂç¶ËȾ·Â¤òľÀÜŪ¤ËÀ©Ì󤹤ë¾ò·ï¼°¤Ç¤¢¤ë¡¥ºÇÂç¶ËȾ·Â¤ò¹Íθ¤·¤¿À߷פǤϡ¤Á«°ÜÉÕ¶á¤Î¿¶Éýδµ¯¤ÎÍÞÀ©¡¤Í¸Â¸ìĹ¤Î¥Õ¥£¥ë¥¿·¸¿ô¤¬µ¯°ø¤·¤¿°ÂÄêÀ¤ÎÁÓ¼º¤ÎËɻߤ¬¼Â¸½¤µ¤ì¤ë¡¥ |
Âê̾ | Åý·×³Ø½¬¤Ë¤è¤ë³Ú²»¥Ç¡¼¥¿¤Î¥¨¥ó¥È¥í¥Ô¡¼Éä¹æ²½¤Î¸¡Æ¤ |
Ãø¼Ô | *À¥Àî ¼þÊ¿ (²¬»³Âç³Ø ¹©³ØÉô ÄÌ¿®¥Í¥Ã¥È¥ï¡¼¥¯¹©³Ø²Ê), »³º¬ ±ä¸µ (²¬»³Âç³Ø ¼«Á³²Ê³Ø¸¦µæ²Ê) |
Page | pp. 437 - 438 |
Keyword | ³Ú²»¥Ç¡¼¥¿, Éä¹æ²½ |
Abstract | ³Ú²»¤Î¶É½ê¿®¹æ¡Ê´Ñ¬¥Ù¥¯¥È¥ë¡Ë¤ËÄê¾ï¥¬¥¦¥¹º®¹ç¥â¥Ç¥ë¡ÊGMM¡Ë¤¬Å¬¹ç¤·¡¤ºÇ¾®Æó¾è¸íº¹¡ÊMMSE¡Ë¿äÄ꤬Àþ·Á¤Ê¥¦¥£¥Ê¡¼¥Õ¥£¥ë¥¿¡ÊWF¡Ë¤Ë¤è¤ê¼Â¸½¤Ç¤¤ë¤³¤È¤¬ÃΤé¤ì¤Æ¤¤¤ë¡£ ËÜÏÀʸ¤Ç¤Ï¡¤³Ú²»¥Ç¡¼¥¿¤Î¥¨¥ó¥È¥í¥Ô¡¼Éä¹æ²½¤Î¼Â¸½¤Ë¸þ¤±¤Æ¤Î½éÊâŪ¤Ê¸¡Æ¤¤È¤·¤Æ¡¢Í½Â¬¸íº¹¤òÆÈΩ²½¤Ç¤¤ë¤³¤È¤ò¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¼Â¸³¤Ë¤è¤Ã¤Æ¸¡¾Ú¤¹¤ë¡£ |
Âê̾ | Ä°³Ð¾ð·ÊʬÀϤ˴ð¤Å¤¯ÊÑ·ÁDFT¤Ë¤è¤ë²»À¼Ê¬Î¥¤Î¸¦µæ ¡½Ä´Çȹ½Â¤¤Ë¤è¤ë¥°¥ë¡¼¥Ô¥ó¥°¤Î»î¤ß¡½ |
Ãø¼Ô | *ÃÓÅÄ ¶©Êå, ÀîÅÄ Î´Ê¿, ²ÖÅÄ ½ã°ì, ÃæÀ¾ ¸ù, Íû »Å¹ä (Ä»¼èÂç³Ø) |
Page | p. 439 |
Keyword | ÊÑ·ÁDFT, Ä°³Ð¾ð·ÊʬÀÏ, ²»À¼Ê¬Î¥ |
Abstract | ¿¤¯¤Î²»¤¬Èô¤Ó¸ò¤¦Ãæ¤Ç¡¤ÆÃÄê¤ÎÁê¼ê¤ÎÀ¼¤À¤±¤ËÃí°Õ¤ò¸þ¤±¤ë¤³¤È¤¬½ÐÍè¤ëǽÎϤò¥«¥¯¥Æ¥ë¥Ñ¡¼¥Æ¥£¸ú²Ì¤È¸À¤¦¡£²»À¼Ê¬Î¥¥·¥¹¥Æ¥à¤ò¼Â¸½¤¹¤ë¾å¤Ç¡¤¥«¥¯¥Æ¥ë¥Ñ¡¼¥Æ¥£¸ú²Ì¤Î¥â¥Ç¥ë²½¤ÏÍÍѤǤ¢¤ë¡£Ëܸ¦µæ¤Ç¤Ï¡¤Ã±°ì¥Þ¥¤¥¯¥í¥Õ¥©¥ó¤ËÆþÎϤµ¤ì¤¿º®¹ç²»À¼¤ò¡¤ÊÑ·ÁDFT¡ÊMDFT¡Ë¤Ë¤è¤Ã¤Æ³Æ¼þÇÈ¿ô¤Î¿®¹æ¤Ëʬ²ò¤·¡¤Ä°³Ð¾ð·ÊʬÀÏ¡ÊASA¡Ë¤Ë´ð¤Å¤¤¤¿¾ò·ï¤òÍѤ¤¤ÆʬΥ¤¹¤ë¤³¤È¤ò»î¤ß¤ë¡£ |
Âê̾ | ǾÇȤòÍѤ¤¤¿±¿Å¾Êä½õ¤Ë´Ø¤¹¤ë¸¦µæ |
Ãø¼Ô | ¹ÃÈå ¹°¿Í, *»³º¬ Âó¿¿, Íû »Å¹ä, ÃæÀ¾ ¸ù, ƣ¼ ´îµ×Ϻ (Ä»¼èÂç³Ø) |
Page | p. 440 |
Keyword | ǾÇÈ, ±¿Å¾Êä½õ |
Abstract | ±¿Å¾¤Ï ¡¢±¿Å¾¼ê¤¬¼þ°Ï¤Îƻϩ¾õ¶·¤ò´Ñ»¡¤·¤Æ ¡¢¤½¤ÎȽÃÇ ¤Ë´ð¤Å¤¤¤Æ¼Ö¤òÁà½Ä¤¹¤ë ¡£¤½¤ÎºÝ ¡¢Ä¾ÀþŪ¤ÊÆ»¤è¤ê¤â¥«¡¼ ¥Ö¡¢¼þ°Ï¤Î¼Öξ¤Î¾¯¤Ê¤¤¹Ù³°¤è¤ê¤â»Ô³¹ÃϤÎÊý¤¬¿À·Ð¤ò½¸ Ã椵¤»¤ëɬÍפ¬¤¢¤ë ¡£¤Ä¤Þ¤ê¡¢±¿Å¾¼ê¤ÎÀº¿À¾õÂ֤Ȥ¤¤¦¤Î ¤Ï¼þ°Ï¤Î¾õ¶·¤È´Ø·¸À¤¬¤¢¤ë¤è¤¦¤Ë»×¤ï¤ì¤ë¡£ ¤½¤³¤ÇËܸ¦µæ¤Ç¤Ï¡¢¤½¤ì¤é¤òÄêÎÌŪ¤Ë·×¬¤¹¤ë»ö¤Ë¤è¤Ã ¤Æ¡¢´Ø·¸À¤ò¸«½Ð¤¹¤³¤È¤ò»î¤ß¤ë¡£ |