Partitioned online sequential extreme learning machine for large ordered system modeling

被引：7
|
作者：
Lim, JunSeok [1 ]
机构：
[1] Sejong Univ, Dept Elect Engn, Seoul 143747, South Korea
来源：
NEUROCOMPUTING | 2013年 / 102卷
基金：
新加坡国家研究基金会;
关键词：
Extreme learning machine; OS-ELM; RLS; Partitioning; SPARSE CHANNEL ESTIMATION; NETWORKS;
D O I：
10.1016/j.neucom.2011.12.049
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we propose an algorithm entitled "partitioned OS-ELM" (P05-ELM) that partitions a large data matrix into small matrices, applies an RLS (Recursive Least Square) scheme in each of the small sub-matrices and assembles the whole estimation vector by the concatenation of the sub-vectors from the RLS outputs of the sub-matrices. Consequently, the algorithm is less complex than the conventional OS-ELM and maintains an almost compatible estimation performance. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码：59 / 64
页数：6
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