Petri net models of fuzzy neural networks
Artificial neural networks (ANN's) are highly parallel and distributed computational structures that can learn from experience and perform inferences. Petri nets, on the other hand, provide an effective modeling framework for distributed systems. The basic concepts of Petri net are utilized to...
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| Veröffentlicht in: | IEEE transactions on systems, man, and cybernetics man, and cybernetics, 1995-06, Vol.25 (6), p.926-932 |
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| container_title | IEEE transactions on systems, man, and cybernetics |
| container_volume | 25 |
| creator | Ahson, S.I. |
| description | Artificial neural networks (ANN's) are highly parallel and distributed computational structures that can learn from experience and perform inferences. Petri nets, on the other hand, provide an effective modeling framework for distributed systems. The basic concepts of Petri net are utilized to develop ANN-like multilayered Petri net architectures of distributed intelligence having learning ability. A Petri net model of single neuron is presented. A two-layer Petri net model-neural Petri net (NPN)-that uses this neuron model as a building block is described. A new class of Petri nets called the fuzzy neural Petri net (FNPN) is defined. The FNPN can be used for representing a fuzzy knowledge base and for fuzzy reasoning. Some application examples for the two Petri net based models are given.< > |
| doi_str_mv | 10.1109/21.384255 |
| format | Article |
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Petri nets, on the other hand, provide an effective modeling framework for distributed systems. The basic concepts of Petri net are utilized to develop ANN-like multilayered Petri net architectures of distributed intelligence having learning ability. A Petri net model of single neuron is presented. A two-layer Petri net model-neural Petri net (NPN)-that uses this neuron model as a building block is described. A new class of Petri nets called the fuzzy neural Petri net (FNPN) is defined. The FNPN can be used for representing a fuzzy knowledge base and for fuzzy reasoning. Some application examples for the two Petri net based models are given.< ></description><identifier>ISSN: 0018-9472</identifier><identifier>EISSN: 2168-2909</identifier><identifier>DOI: 10.1109/21.384255</identifier><identifier>CODEN: ISYMAW</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Applied sciences ; Artificial intelligence ; Artificial neural networks ; Computer architecture ; Computer networks ; Computer science; control theory; systems ; Concurrent computing ; Connectionism. Neural networks ; Distributed computing ; Exact sciences and technology ; Fuzzy neural networks ; Fuzzy reasoning ; High performance computing ; Neurons ; Petri nets</subject><ispartof>IEEE transactions on systems, man, and cybernetics, 1995-06, Vol.25 (6), p.926-932</ispartof><rights>1995 INIST-CNRS</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c275t-7f58af68f9fbaf2c9c4d565d69409b729c09ecf59b6caba84fce0f8cec8be36d3</citedby><cites>FETCH-LOGICAL-c275t-7f58af68f9fbaf2c9c4d565d69409b729c09ecf59b6caba84fce0f8cec8be36d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/384255$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,777,781,793,27905,27906,54739</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/384255$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=3533488$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Ahson, S.I.</creatorcontrib><title>Petri net models of fuzzy neural networks</title><title>IEEE transactions on systems, man, and cybernetics</title><addtitle>T-SMC</addtitle><description>Artificial neural networks (ANN's) are highly parallel and distributed computational structures that can learn from experience and perform inferences. Petri nets, on the other hand, provide an effective modeling framework for distributed systems. The basic concepts of Petri net are utilized to develop ANN-like multilayered Petri net architectures of distributed intelligence having learning ability. A Petri net model of single neuron is presented. A two-layer Petri net model-neural Petri net (NPN)-that uses this neuron model as a building block is described. A new class of Petri nets called the fuzzy neural Petri net (FNPN) is defined. The FNPN can be used for representing a fuzzy knowledge base and for fuzzy reasoning. Some application examples for the two Petri net based models are given.< ></description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Computer architecture</subject><subject>Computer networks</subject><subject>Computer science; control theory; systems</subject><subject>Concurrent computing</subject><subject>Connectionism. Neural networks</subject><subject>Distributed computing</subject><subject>Exact sciences and technology</subject><subject>Fuzzy neural networks</subject><subject>Fuzzy reasoning</subject><subject>High performance computing</subject><subject>Neurons</subject><subject>Petri nets</subject><issn>0018-9472</issn><issn>2168-2909</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1995</creationdate><recordtype>article</recordtype><recordid>eNpFj81Lw0AQxRdRMFYPXj3l4KWH1P3O7lGKX1DQg57DZjID0bQpuynS_vWmRPT0mPd-8-Axdi34Qgju76RYKKelMScsk8K6QnruT1nGuXCF16U8ZxcpfY6n1t5kbP6GQ2zzDQ75um-wS3lPOe0Oh_3o7WLojtF3H7_SJTuj0CW8-tUZ-3h8eF8-F6vXp5fl_aoAWZqhKMm4QNaRpzqQBA-6MdY01mvu61J64B6BjK8thDo4TYCcHCC4GpVt1IzNp16IfUoRqdrGdh3ivhK8Om6spKimjSN7O7HbkCB0FMMG2vT3oIxS2rkRu5mwFhH_06njB-HlWT4</recordid><startdate>19950601</startdate><enddate>19950601</enddate><creator>Ahson, S.I.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>19950601</creationdate><title>Petri net models of fuzzy neural networks</title><author>Ahson, S.I.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c275t-7f58af68f9fbaf2c9c4d565d69409b729c09ecf59b6caba84fce0f8cec8be36d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1995</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Artificial neural networks</topic><topic>Computer architecture</topic><topic>Computer networks</topic><topic>Computer science; control theory; systems</topic><topic>Concurrent computing</topic><topic>Connectionism. Neural networks</topic><topic>Distributed computing</topic><topic>Exact sciences and technology</topic><topic>Fuzzy neural networks</topic><topic>Fuzzy reasoning</topic><topic>High performance computing</topic><topic>Neurons</topic><topic>Petri nets</topic><toplevel>online_resources</toplevel><creatorcontrib>Ahson, S.I.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><jtitle>IEEE transactions on systems, man, and cybernetics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ahson, S.I.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Petri net models of fuzzy neural networks</atitle><jtitle>IEEE transactions on systems, man, and cybernetics</jtitle><stitle>T-SMC</stitle><date>1995-06-01</date><risdate>1995</risdate><volume>25</volume><issue>6</issue><spage>926</spage><epage>932</epage><pages>926-932</pages><issn>0018-9472</issn><eissn>2168-2909</eissn><coden>ISYMAW</coden><abstract>Artificial neural networks (ANN's) are highly parallel and distributed computational structures that can learn from experience and perform inferences. Petri nets, on the other hand, provide an effective modeling framework for distributed systems. The basic concepts of Petri net are utilized to develop ANN-like multilayered Petri net architectures of distributed intelligence having learning ability. A Petri net model of single neuron is presented. A two-layer Petri net model-neural Petri net (NPN)-that uses this neuron model as a building block is described. A new class of Petri nets called the fuzzy neural Petri net (FNPN) is defined. The FNPN can be used for representing a fuzzy knowledge base and for fuzzy reasoning. Some application examples for the two Petri net based models are given.< ></abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/21.384255</doi><tpages>7</tpages></addata></record> |
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| ispartof | IEEE transactions on systems, man, and cybernetics, 1995-06, Vol.25 (6), p.926-932 |
| issn | 0018-9472 2168-2909 |
| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Applied sciences Artificial intelligence Artificial neural networks Computer architecture Computer networks Computer science control theory systems Concurrent computing Connectionism. Neural networks Distributed computing Exact sciences and technology Fuzzy neural networks Fuzzy reasoning High performance computing Neurons Petri nets |
| title | Petri net models of fuzzy neural networks |
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