Hybrid Quaternionic Hopfield Neural Network

In recent years, applications of complex-valued neural networks have become wide spread. Quaternions are an extension of complex numbers, and neural networks with quaternions have been proposed. Because quaternion algebra is non-commutative algebra, we can consider two orders of multiplication to ca...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences Communications and Computer Sciences, 2015/07/01, Vol.E98.A(7), pp.1512-1518
1. Verfasser: KOBAYASHI, Masaki
Format: Artikel
Sprache:eng ; jpn
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 1518
container_issue 7
container_start_page 1512
container_title IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
container_volume E98.A
creator KOBAYASHI, Masaki
description In recent years, applications of complex-valued neural networks have become wide spread. Quaternions are an extension of complex numbers, and neural networks with quaternions have been proposed. Because quaternion algebra is non-commutative algebra, we can consider two orders of multiplication to calculate weighted input. However, both orders provide almost the same performance. We propose hybrid quaternionic Hopfield neural networks, which have both orders of multiplication. Using computer simulations, we show that these networks outperformed conventional quaternionic Hopfield neural networks in noise tolerance. We discuss why hybrid quaternionic Hopfield neural networks improve noise tolerance from the standpoint of rotational invariance.
doi_str_mv 10.1587/transfun.E98.A.1512
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1823941047</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1823941047</sourcerecordid><originalsourceid>FETCH-LOGICAL-c548t-2459cc35f0d45de4323e0ac7dc8e980d3ec1138f6abcaf4b9c0b2525ccf54803</originalsourceid><addsrcrecordid>eNqFkMFKAzEQQIMoWKtf4KVHQbYmm6RJjqVUWyiK0HvIzk5063a3JlnEv3dLtYgXTwPDe8PwCLlmdMykVncpuCb6rhnPjR5P-x3LT8iAKSEzxrk6JQNq2CTTkupzchHjhlKmcyYG5HbxWYSqHD13LmFoqrapYLRod77Cuhw9Yhdc3Y_00Ya3S3LmXR3x6nsOyfp-vp4tstXTw3I2XWUghU5ZLqQB4NLTUsgSBc85UgeqBI1G05IjMMa1n7gCnBeFAVrkMpcAvvcpH5Kbw9ldaN87jMluqwhY167Btou2_5wbwahQ_6OKaSMmWsoe5QcUQhtjQG93odq68GkZtfuI9iei7SPaqd1H7K3lwdrE5F7w6LiQKqjxr6N-uUcGXl2w2PAvk_GCCA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1718946855</pqid></control><display><type>article</type><title>Hybrid Quaternionic Hopfield Neural Network</title><source>J-STAGE (Japan Science &amp; Technology Information Aggregator, Electronic) Freely Available Titles - Japanese</source><creator>KOBAYASHI, Masaki</creator><creatorcontrib>KOBAYASHI, Masaki</creatorcontrib><description>In recent years, applications of complex-valued neural networks have become wide spread. Quaternions are an extension of complex numbers, and neural networks with quaternions have been proposed. Because quaternion algebra is non-commutative algebra, we can consider two orders of multiplication to calculate weighted input. However, both orders provide almost the same performance. We propose hybrid quaternionic Hopfield neural networks, which have both orders of multiplication. Using computer simulations, we show that these networks outperformed conventional quaternionic Hopfield neural networks in noise tolerance. We discuss why hybrid quaternionic Hopfield neural networks improve noise tolerance from the standpoint of rotational invariance.</description><identifier>ISSN: 0916-8508</identifier><identifier>EISSN: 1745-1337</identifier><identifier>DOI: 10.1587/transfun.E98.A.1512</identifier><language>eng ; jpn</language><publisher>The Institute of Electronics, Information and Communication Engineers</publisher><subject>Algebra ; complex-valued associative memory ; Computer simulation ; Hopfield neural networks ; Mathematical analysis ; Multiplication ; Neural networks ; Noise tolerance ; quaternion ; Quaternions ; rotational invariance ; Spreads</subject><ispartof>IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 2015/07/01, Vol.E98.A(7), pp.1512-1518</ispartof><rights>2015 The Institute of Electronics, Information and Communication Engineers</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c548t-2459cc35f0d45de4323e0ac7dc8e980d3ec1138f6abcaf4b9c0b2525ccf54803</citedby><cites>FETCH-LOGICAL-c548t-2459cc35f0d45de4323e0ac7dc8e980d3ec1138f6abcaf4b9c0b2525ccf54803</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,1882,4023,27922,27923,27924</link.rule.ids></links><search><creatorcontrib>KOBAYASHI, Masaki</creatorcontrib><title>Hybrid Quaternionic Hopfield Neural Network</title><title>IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences</title><addtitle>IEICE Trans. Fundamentals</addtitle><description>In recent years, applications of complex-valued neural networks have become wide spread. Quaternions are an extension of complex numbers, and neural networks with quaternions have been proposed. Because quaternion algebra is non-commutative algebra, we can consider two orders of multiplication to calculate weighted input. However, both orders provide almost the same performance. We propose hybrid quaternionic Hopfield neural networks, which have both orders of multiplication. Using computer simulations, we show that these networks outperformed conventional quaternionic Hopfield neural networks in noise tolerance. We discuss why hybrid quaternionic Hopfield neural networks improve noise tolerance from the standpoint of rotational invariance.</description><subject>Algebra</subject><subject>complex-valued associative memory</subject><subject>Computer simulation</subject><subject>Hopfield neural networks</subject><subject>Mathematical analysis</subject><subject>Multiplication</subject><subject>Neural networks</subject><subject>Noise tolerance</subject><subject>quaternion</subject><subject>Quaternions</subject><subject>rotational invariance</subject><subject>Spreads</subject><issn>0916-8508</issn><issn>1745-1337</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNqFkMFKAzEQQIMoWKtf4KVHQbYmm6RJjqVUWyiK0HvIzk5063a3JlnEv3dLtYgXTwPDe8PwCLlmdMykVncpuCb6rhnPjR5P-x3LT8iAKSEzxrk6JQNq2CTTkupzchHjhlKmcyYG5HbxWYSqHD13LmFoqrapYLRod77Cuhw9Yhdc3Y_00Ya3S3LmXR3x6nsOyfp-vp4tstXTw3I2XWUghU5ZLqQB4NLTUsgSBc85UgeqBI1G05IjMMa1n7gCnBeFAVrkMpcAvvcpH5Kbw9ldaN87jMluqwhY167Btou2_5wbwahQ_6OKaSMmWsoe5QcUQhtjQG93odq68GkZtfuI9iei7SPaqd1H7K3lwdrE5F7w6LiQKqjxr6N-uUcGXl2w2PAvk_GCCA</recordid><startdate>2015</startdate><enddate>2015</enddate><creator>KOBAYASHI, Masaki</creator><general>The Institute of Electronics, Information and Communication Engineers</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7QO</scope><scope>FR3</scope><scope>P64</scope></search><sort><creationdate>2015</creationdate><title>Hybrid Quaternionic Hopfield Neural Network</title><author>KOBAYASHI, Masaki</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c548t-2459cc35f0d45de4323e0ac7dc8e980d3ec1138f6abcaf4b9c0b2525ccf54803</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng ; jpn</language><creationdate>2015</creationdate><topic>Algebra</topic><topic>complex-valued associative memory</topic><topic>Computer simulation</topic><topic>Hopfield neural networks</topic><topic>Mathematical analysis</topic><topic>Multiplication</topic><topic>Neural networks</topic><topic>Noise tolerance</topic><topic>quaternion</topic><topic>Quaternions</topic><topic>rotational invariance</topic><topic>Spreads</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>KOBAYASHI, Masaki</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology Research Abstracts</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>KOBAYASHI, Masaki</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Hybrid Quaternionic Hopfield Neural Network</atitle><jtitle>IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences</jtitle><addtitle>IEICE Trans. Fundamentals</addtitle><date>2015</date><risdate>2015</risdate><volume>E98.A</volume><issue>7</issue><spage>1512</spage><epage>1518</epage><pages>1512-1518</pages><issn>0916-8508</issn><eissn>1745-1337</eissn><abstract>In recent years, applications of complex-valued neural networks have become wide spread. Quaternions are an extension of complex numbers, and neural networks with quaternions have been proposed. Because quaternion algebra is non-commutative algebra, we can consider two orders of multiplication to calculate weighted input. However, both orders provide almost the same performance. We propose hybrid quaternionic Hopfield neural networks, which have both orders of multiplication. Using computer simulations, we show that these networks outperformed conventional quaternionic Hopfield neural networks in noise tolerance. We discuss why hybrid quaternionic Hopfield neural networks improve noise tolerance from the standpoint of rotational invariance.</abstract><pub>The Institute of Electronics, Information and Communication Engineers</pub><doi>10.1587/transfun.E98.A.1512</doi><tpages>7</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0916-8508
ispartof IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 2015/07/01, Vol.E98.A(7), pp.1512-1518
issn 0916-8508
1745-1337
language eng ; jpn
recordid cdi_proquest_miscellaneous_1823941047
source J-STAGE (Japan Science & Technology Information Aggregator, Electronic) Freely Available Titles - Japanese
subjects Algebra
complex-valued associative memory
Computer simulation
Hopfield neural networks
Mathematical analysis
Multiplication
Neural networks
Noise tolerance
quaternion
Quaternions
rotational invariance
Spreads
title Hybrid Quaternionic Hopfield Neural Network
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-08T18%3A17%3A05IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Hybrid%20Quaternionic%20Hopfield%20Neural%20Network&rft.jtitle=IEICE%20Transactions%20on%20Fundamentals%20of%20Electronics,%20Communications%20and%20Computer%20Sciences&rft.au=KOBAYASHI,%20Masaki&rft.date=2015&rft.volume=E98.A&rft.issue=7&rft.spage=1512&rft.epage=1518&rft.pages=1512-1518&rft.issn=0916-8508&rft.eissn=1745-1337&rft_id=info:doi/10.1587/transfun.E98.A.1512&rft_dat=%3Cproquest_cross%3E1823941047%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1718946855&rft_id=info:pmid/&rfr_iscdi=true