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Current Result Document :

ÇѱÛÁ¦¸ñ(Korean Title) ·£´ý ½Éº¼¿¡ ±â¹ÝÇÑ Á¤º¸ÀÌ·ÐÀû ÇнÀ¹ýÀÇ ½ºÅÜ »çÀÌÁî Á¤±ÔÈ­
¿µ¹®Á¦¸ñ(English Title) Step-size Normalization of Information Theoretic Learning Methods based on Random Symbols
ÀúÀÚ(Author) ±è³²¿ë   Namyong Kim  
¿ø¹®¼ö·Ïó(Citation) VOL 21 NO. 02 PP. 0049 ~ 0055 (2020. 04)
Çѱ۳»¿ë
(Korean Abstract)
·£´ý ½Éº¼¿­À» ±â¹ÝÀ¸·Î ÇÑ Á¤º¸ÀÌ·ÐÀû ÇнÀ¹ý (ITL)Àº ƯÁ¤ È®·üºÐÆ÷¸¦ °®µµ·Ï ·£´ýÇÏ°Ô ¹ß»ý½ÃŲ ½Éº¼¿­À» Ÿ°Ù µ¥ÀÌÅÍ·Î È°¿ëÇÏ°í, ÀÔ·Â µ¥ÀÌÅÍ »çÀÌÀÇ È®·üºÐÆ÷ °Å¸® ÃÖ¼ÒÈ­¸¦ ºñ¿ëÇÔ¼ö·Î ÇÏ¿© ¼³°èµÈ´Ù. ÀÌ ¹æ½ÄÀÇ ´ÜÁ¡À¸·Î, °íÁ¤»ó¼ö¸¦ ¾Ë°í¸®µë °»½ÅÀÇ ½ºÅÜ»çÀÌÁî·Î »ç¿ëÇϹǷΠÀÔ·Â Àü·ÂÀÇ Åë°èÀû ÃßÀ̸¦ È°¿ëÇÒ ¼ö ¾ø´Ù. Á¤º¸Æ÷ÅÙ¼È Ãâ·Â(information potential output, IPO)¿Í ¿¬°üµÈ ±â¿ï±â¿¡¼­´Â Á¤º¸Æ÷ÅÙ¼È ÀÔ·Â(information potential input, IPI)ÀÌ, Á¤º¸Æ÷ÅÙ¼È ¿ÀÂ÷(information potential error, IPE)¿Í °ü·ÃµÈ ±â¿ï±â¿¡¼­´Â ÀÔ·ÂÀÚü°¡ ÀÔ·ÂÀ¸·Î ÀÛ¿ëÇÔÀ» ÀÌ ¿¬±¸¿¡¼­ ¹àÇô³»°í, ÀÔ·ÂÀÇ Àü·Â ÃßÀ̸¦ µû·Î °è»êÇÏ¿© ½ºÅÜ»çÀÌÁî (step size)¸¦ Á¤±ÔÈ­Çϵµ·Ï Á¦¾ÈÇÏ¿´´Ù. Á¦¾ÈµÈ ¾Ë°í¸®µëÀº Ãæ°Ý¼ºÀâÀ½°ú ´ÙÁß°æ·Î ÆäÀ̵ù ȯ°æÀÇ Åë½Å½Ã½ºÅÛ ½ÇÇè¿¡¼­ ±âÁ¸ ¹æ½Äº¸´Ù ¾à 4dB Á¤µµ ´õ ³·Àº Á¤»ó»óÅ ¿ÀÂ÷ Àü·Â, ¾à 2¹è ÀÌ»ó ºü¸¥ ¼ö·Å¼Óµµ¸¦ ³ªÅ¸³Â´Ù.
¿µ¹®³»¿ë
(English Abstract)
Information theoretic learning (ITL) methods based on random symbols (RS) use a set of random symbols generated according to a target distribution and are designed nonparametrically to minimize the cost function of the Euclidian distance between the target distribution and the input distribution. One drawback of the learning method is that it can not utilize the input power statistics by employing a constant stepsize for updating the algorithm. In this paper, it is revealed that firstly, information potential input (IPI) plays a role of input in the cost function-derivative related with information potential output (IPO) and secondly, input itself does in the derivative related with information potential error (IPE). Based on these observations, it is proposed to normalize the step-size with the statistically varying power of the two different inputs, IPI and input itself. The proposed algorithm in an communication environment of impulsive noise and multipath fading shows that the performance of mean squared error (MSE) is lower by 4dB, and convergence speed is 2 times faster than the conventional methods without step-size normalization.
Å°¿öµå(Keyword) ºÐÆ÷°Å¸®   ·£´ý½Éº¼   ÀÔ·Â Àü·Â Á¤±ÔÈ­   ½ºÅÜ»çÀÌÁî   Á¤º¸Æ÷ÅټȠ  Ãæ°Ý¼º ÀâÀ½   Distribution distance   random symbol   step-size   information potential   Impulsive noise  
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