A Hybrid Blind Signal Separation Algorithm: Particle Swarm Optimization on Feed-Forward Neural Network
The blind signal separation problem (BSS) which involved linear mixing model and stationary source signals is focused in this paper. In the past, the neural network (NN) model is the popular architecture for separation, but its performance depends on initiation of weight strongly. In order to improv...
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| creator | Liu, Chan-Cheng Sun, Tsung-Ying Hsieh, Sheng-Ta Lin, Chun-Ling Lee, Kan-Yuan |
| description | The blind signal separation problem (BSS) which involved linear mixing model and stationary source signals is focused in this paper. In the past, the neural network (NN) model is the popular architecture for separation, but its performance depends on initiation of weight strongly. In order to improve this problem to enhance global convergent, the genetic algorithm (GA) has been introduced for optimizing the weights of NN system recently. This paper, a novel evolution algorithm, particle swarm optimization (PSO) is introduced to optimize NN weights by us. Further, in simulation experiments of BSS, it is demonstrated that the PSO-based NN system has better performance in terms of global searching, computational time, accuracy and efficiency than the GA-based NN system. |
| doi_str_mv | 10.1007/11893028_120 |
| format | Conference Proceeding |
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In the past, the neural network (NN) model is the popular architecture for separation, but its performance depends on initiation of weight strongly. In order to improve this problem to enhance global convergent, the genetic algorithm (GA) has been introduced for optimizing the weights of NN system recently. This paper, a novel evolution algorithm, particle swarm optimization (PSO) is introduced to optimize NN weights by us. Further, in simulation experiments of BSS, it is demonstrated that the PSO-based NN system has better performance in terms of global searching, computational time, accuracy and efficiency than the GA-based NN system.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 3540464794</identifier><identifier>ISBN: 9783540464792</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 9783540464808</identifier><identifier>EISBN: 3540464808</identifier><identifier>DOI: 10.1007/11893028_120</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Connectionism. Neural networks ; Exact sciences and technology ; Genetic Algorithm ; Independent Component Analysis ; Particle Swarm Optimization ; Radial Basis Function Neural Network</subject><ispartof>Lecture notes in computer science, 2006, p.1078-1087</ispartof><rights>Springer-Verlag Berlin Heidelberg 2006</rights><rights>2008 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/11893028_120$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/11893028_120$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>309,310,779,780,784,789,790,793,4048,4049,27924,38254,41441,42510</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=19969702$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><contributor>Wang, Jun</contributor><contributor>King, Irwin</contributor><contributor>Chan, Lai-Wan</contributor><contributor>Wang, DeLiang</contributor><creatorcontrib>Liu, Chan-Cheng</creatorcontrib><creatorcontrib>Sun, Tsung-Ying</creatorcontrib><creatorcontrib>Hsieh, Sheng-Ta</creatorcontrib><creatorcontrib>Lin, Chun-Ling</creatorcontrib><creatorcontrib>Lee, Kan-Yuan</creatorcontrib><title>A Hybrid Blind Signal Separation Algorithm: Particle Swarm Optimization on Feed-Forward Neural Network</title><title>Lecture notes in computer science</title><description>The blind signal separation problem (BSS) which involved linear mixing model and stationary source signals is focused in this paper. In the past, the neural network (NN) model is the popular architecture for separation, but its performance depends on initiation of weight strongly. In order to improve this problem to enhance global convergent, the genetic algorithm (GA) has been introduced for optimizing the weights of NN system recently. This paper, a novel evolution algorithm, particle swarm optimization (PSO) is introduced to optimize NN weights by us. Further, in simulation experiments of BSS, it is demonstrated that the PSO-based NN system has better performance in terms of global searching, computational time, accuracy and efficiency than the GA-based NN system.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Connectionism. Neural networks</subject><subject>Exact sciences and technology</subject><subject>Genetic Algorithm</subject><subject>Independent Component Analysis</subject><subject>Particle Swarm Optimization</subject><subject>Radial Basis Function Neural Network</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>3540464794</isbn><isbn>9783540464792</isbn><isbn>9783540464808</isbn><isbn>3540464808</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2006</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpNkE1Lw0AQhtcvsK29-QP24kWIzn4km_FWi7VCaYXqOWx2N3VtmoRNpNRfb6QehIEZeJ55Dy8h1wzuGIC6ZyxFATzNGIcTMkaViliCTGQK6SkZsISxSAiJZ2T4BxTKczKA_ilCJcUlGbbtJwBwhXxAigmdH_LgLX0sfWXp2m8qXdK1a3TQna8rOik3dfDdx-6BvurQeVM6ut7rsKOrpvM7_33U-pk5Z6NZHXpo6dJ9hT5o6bp9HbZX5KLQZevGf3tE3mdPb9N5tFg9v0wni6jhHLoIIYUCJCiBiUuYjFNkKPubWW7QcYssLWJrnDWy4MZILaVDnrMYXeFULkbk5pjb6Nbosgi6Mr7NmuB3OhwyhpigAt57t0ev7VG1cSHL63rbZgyy35qz_zWLH9q6alM</recordid><startdate>2006</startdate><enddate>2006</enddate><creator>Liu, Chan-Cheng</creator><creator>Sun, Tsung-Ying</creator><creator>Hsieh, Sheng-Ta</creator><creator>Lin, Chun-Ling</creator><creator>Lee, Kan-Yuan</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2006</creationdate><title>A Hybrid Blind Signal Separation Algorithm: Particle Swarm Optimization on Feed-Forward Neural Network</title><author>Liu, Chan-Cheng ; Sun, Tsung-Ying ; Hsieh, Sheng-Ta ; Lin, Chun-Ling ; Lee, Kan-Yuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p220t-9080f0407396e61458919496e1d2c9e2d918f5dcedc4f2cc4a44e92b159efe7b3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Connectionism. Neural networks</topic><topic>Exact sciences and technology</topic><topic>Genetic Algorithm</topic><topic>Independent Component Analysis</topic><topic>Particle Swarm Optimization</topic><topic>Radial Basis Function Neural Network</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Chan-Cheng</creatorcontrib><creatorcontrib>Sun, Tsung-Ying</creatorcontrib><creatorcontrib>Hsieh, Sheng-Ta</creatorcontrib><creatorcontrib>Lin, Chun-Ling</creatorcontrib><creatorcontrib>Lee, Kan-Yuan</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Chan-Cheng</au><au>Sun, Tsung-Ying</au><au>Hsieh, Sheng-Ta</au><au>Lin, Chun-Ling</au><au>Lee, Kan-Yuan</au><au>Wang, Jun</au><au>King, Irwin</au><au>Chan, Lai-Wan</au><au>Wang, DeLiang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A Hybrid Blind Signal Separation Algorithm: Particle Swarm Optimization on Feed-Forward Neural Network</atitle><btitle>Lecture notes in computer science</btitle><date>2006</date><risdate>2006</risdate><spage>1078</spage><epage>1087</epage><pages>1078-1087</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>3540464794</isbn><isbn>9783540464792</isbn><eisbn>9783540464808</eisbn><eisbn>3540464808</eisbn><abstract>The blind signal separation problem (BSS) which involved linear mixing model and stationary source signals is focused in this paper. In the past, the neural network (NN) model is the popular architecture for separation, but its performance depends on initiation of weight strongly. In order to improve this problem to enhance global convergent, the genetic algorithm (GA) has been introduced for optimizing the weights of NN system recently. This paper, a novel evolution algorithm, particle swarm optimization (PSO) is introduced to optimize NN weights by us. Further, in simulation experiments of BSS, it is demonstrated that the PSO-based NN system has better performance in terms of global searching, computational time, accuracy and efficiency than the GA-based NN system.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11893028_120</doi><tpages>10</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Lecture notes in computer science, 2006, p.1078-1087 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_19969702 |
| source | Springer Books |
| subjects | Applied sciences Artificial intelligence Computer science control theory systems Connectionism. Neural networks Exact sciences and technology Genetic Algorithm Independent Component Analysis Particle Swarm Optimization Radial Basis Function Neural Network |
| title | A Hybrid Blind Signal Separation Algorithm: Particle Swarm Optimization on Feed-Forward Neural Network |
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