Parallel extreme learning machine based on improved particle swarm optimization - Details

author：

Li, W. (Li, W..) ^[1] | Chen, Y. (Chen, Y..) ^[2] (Scholars：陈羽中) | Guo, K. (Guo, K..) ^[3] (Scholars：郭昆) | Guo, S. (Guo, S..) ^[4] | Liu, Z. (Liu, Z..) ^[5] (Scholars：刘漳辉)

Indexed by：

Scopus PKU CSCD

Abstract：

To　improve　the　stability　of　extreme　learning　machine(ELM),　an　extreme　learning　machine　based　on　improved　particle　swarm　optimization　(IPSO-ELM)　is　proposed.　By　combining　the　improved　particle　swarm　optimization　with　ELM,　IPSO-ELM　can　find　the　optimal　number　of　nodes　in　the　hidden　layer　as　well　as　the　optimal　input　weights　and　hidden　biases.　Furthermore,　a　mutation　operator　is　introduced　into　IPSO-ELM　to　enhance　the　diversity　of　swarm　and　improve　the　convergence　speed　of　the　random　search　process.　Then,　to　handle　the　large-scale　electrical　load　data,　a　parallel　version　of　IPSO-ELM　named　PIPSO-ELM　is　implemented　with　the　popular　parallel　computing　framework　Spark.　Experimental　results　of　real-life　electrical　load　data　show　that　PIPSO-ELM　obtains　better　stability　and　scalability　with　higher　efficiency　in　large-scale　electrical　load　prediction.　©　2016,　Science　Press.　All　right　reserved.

Keyword：

Electrical Load Prediction; Extreme Learning Machine(ELM); Mutation Operator; Parallel Computation; Particle Swarm Optimization (PSO)

Community：

[ 1 ] [Li, W.]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350116, China
[ 2 ] [Li, W.]Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou, 350116, China
[ 3 ] [Li, W.]Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou, 350003, China
[ 4 ] [Chen, Y.]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350116, China
[ 5 ] [Chen, Y.]Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou, 350116, China
[ 6 ] [Chen, Y.]Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou, 350003, China
[ 7 ] [Guo, K.]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350116, China
[ 8 ] [Guo, K.]Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou, 350116, China
[ 9 ] [Guo, K.]Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou, 350003, China
[ 10 ] [Guo, S.]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350116, China
[ 11 ] [Guo, S.]Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou, 350116, China
[ 12 ] [Guo, S.]Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou, 350003, China
[ 13 ] [Liu, Z.]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350116, China
[ 14 ] [Liu, Z.]Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou, 350116, China

Reprint 's Address：

陈羽中
[Chen, Y.]College of Mathematics and Computer Science, Fuzhou UniversityChina

Email：

yzchen@fzu.edu.cn

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Source ：

Pattern Recognition and Artificial Intelligence

ISSN： 1003-6059

CN： 34-1089/TP

Year： 2016

Issue： 9

Volume： 29

Page： 840-849

Cited Count：

WoS CC Cited Count：

SCOPUS Cited Count： 14

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 0

Affiliated Colleges：

数学与统计学院本学院/部未明确归属的数据

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