| ²é¿´: 209 | »Ø¸´: 2 | ||
| ±¾Ìû²úÉú 1 ¸ö £¬µã»÷ÕâÀï½øÐв鿴 | ||
mingze0108ͳæ (СÓÐÃûÆø)
|
[ÇóÖú]
Çó°ïæ¼ìË÷¸ÃÎÄÕµļìË÷ºÅ
|
|
|
»ùÓÚSigmoid¶þ´ÎÐÍÁ¥Êô¶Èº¯ÊýµÄ¸Ä½øLMSËã·¨ ·¢×ÔСľ³æIOS¿Í»§¶Ë |
» ²ÂÄãϲ»¶
286Çóµ÷¼Á
ÒѾÓÐ25È˻ظ´
22ר˶Çóµ÷¼Á
ÒѾÓÐ8È˻ظ´
²ÄÁϹ¤³Ì281»¹Óе÷¼Á»ú»áÂð
ÒѾÓÐ19È˻ظ´
ҩѧ305Çóµ÷¼Á
ÒѾÓÐ7È˻ظ´
290Çóµ÷¼Á
ÒѾÓÐ13È˻ظ´
085410 273Çóµ÷¼Á
ÒѾÓÐ8È˻ظ´
0831Ò»ÂÖµ÷¼Áʧ°ÜÇóÖú
ÒѾÓÐ3È˻ظ´
271Çóµ÷¼Á
ÒѾÓÐ22È˻ظ´
Çóµ÷¼Á£¬985²ÄÁÏÓ뻯¹¤348·Ö
ÒѾÓÐ9È˻ظ´
344 ²ÄÁÏרҵ Çóµ÷¼Á211 ÎÞµØÓòÒªÇó
ÒѾÓÐ5È˻ظ´
baiyuefei
°æÖ÷ (ÎÄѧ̩¶·)
·çÑ©
- Ó¦Öú: 4642 (¸±½ÌÊÚ)
- ¹ó±ö: 46.97
- ½ð±Ò: 658711
- É¢½ð: 11616
- ºì»¨: 995
- ɳ·¢: 81
- Ìû×Ó: 69434
- ÔÚÏß: 13334.1Сʱ
- ³æºÅ: 676696
- ×¢²á: 2008-12-18
- ÐÔ±ð: GG
- רҵ: ºÏ³ÉÒ©Îﻯѧ
- ¹ÜϽ: Óлú½»Á÷
¡¾´ð°¸¡¿Ó¦Öú»ØÌû
¡ï ¡ï ¡ï ¡ï ¡ï
¸Ðл²ÎÓ룬ӦÖúÖ¸Êý +1
mingze0108(lazy½õϪ´ú·¢): ½ð±Ò+5, ÐÖú½áÌû£¬¸ÐлӦÖú£¡ 2016-05-27 08:56:50
lazy½õϪ: LS-EPI+1, ¸ÐлӦÖú£¡ 2016-05-27 08:56:55
¸Ðл²ÎÓ룬ӦÖúÖ¸Êý +1
mingze0108(lazy½õϪ´ú·¢): ½ð±Ò+5, ÐÖú½áÌû£¬¸ÐлӦÖú£¡ 2016-05-27 08:56:50
lazy½õϪ: LS-EPI+1, ¸ÐлӦÖú£¡ 2016-05-27 08:56:55
|
ccession number: 20150200412777 Title: Improved LMS algorithm based on Sigmoid quadratic membership function Authors: Xu, Yang1 Email author mingze0108@126.com; Xu, Songtao1; Ma, Jian1; Yang, Yongjian1; Xiao, Bingsong1; Xiang, Jianjun1 Author affiliation: 1 College of Aeronautics and Astronautics Engineering, Air Force Engineering University, Xi'an , China Corresponding author: Xu, Yang Source title: Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) Abbreviated source title: Zhongnan Daxue Xuebao (Ziran Kexue Ban) Volume: 45 Issue: 10 Issue date: October 26, 2014 Publication year: 2014 Pages: 3470-3476 Language: Chinese ISSN: 16727207 CODEN: ZDXZAC Document type: Journal article (JA) Publisher: Central South University of Technology Abstract: The least mean square (LMS) algorithm based on S-function some advantages such as has a small amount of calculation, high convergence rate and good tracking performance for time-varying systems. But when the signal's error is small, the step factor changes too fast, and system identification is not quick enough and the controllable variables are few. To solve the above shortcomings, an algorithm based on the sigmoid quadratic membership function was put forward. The results show that convergence rate of the algorithm is superior to other improved algorithms based on the S-function, and the tracking performance of the time-varying system is better than the improved normalized LMS algorithms. The algorithm put forward in this paper not only overcomes the contradiction between the signal's error and step factor, but also makes the algorithm more flexible by introducing new controllable variable. Number of references: 25 Main heading: Membership functions Controlled terms: Algorithms - Identification (control systems) - Religious buildings - Time varying systems Uncontrolled terms: Convergence rates - Least mean square algorithms - LMS algorithms - Normalized LMS - S function - System tracking - Tracking performance - Variable step size Classification code: 402.2 Public Buildings - 731.1 Control Systems - 921 Mathematics Database: Compendex Compilation and indexing terms, © 2016 Elsevier Inc. |
2Â¥2016-05-24 15:10:24
mingze0108
ͳæ (СÓÐÃûÆø)
- Ó¦Öú: 0 (Ó×¶ùÔ°)
- ½ð±Ò: 2843.6
- Ìû×Ó: 255
- ÔÚÏß: 47.3Сʱ
- ³æºÅ: 2457630
- ×¢²á: 2013-05-10
- ÐÔ±ð: GG
- רҵ: ÐźÅÀíÂÛÓëÐźŴ¦Àí
3Â¥2016-05-24 15:43:07













»Ø¸´´ËÂ¥