Supervised and Unsupervised Learning by Using Petri Nets
Artificial neural networks (ANN) are developed for highly parallel and distributed systems. These systems are able to learn from experience and to perform inferences. Although Petri nets (PNs) were modified to be ANN-like multilayered architectures for fuzzy reasoning, some researchers have paid mor...
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| Veröffentlicht in: | IEEE transactions on systems, man and cybernetics. Part A, Systems and humans man and cybernetics. Part A, Systems and humans, 2010-03, Vol.40 (2), p.363-375 |
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| container_title | IEEE transactions on systems, man and cybernetics. Part A, Systems and humans |
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| creator | Shen, V. Yue-Shan Chang Juang, T.T.-Y. |
| description | Artificial neural networks (ANN) are developed for highly parallel and distributed systems. These systems are able to learn from experience and to perform inferences. Although Petri nets (PNs) were modified to be ANN-like multilayered architectures for fuzzy reasoning, some researchers have paid more attention to the PN-based learning so far. In this paper, we have developed supervised and unsupervised learning algorithms for the machine learning PN (MLPN) models in order to make them fully trainable and to remedy the difficulties encountered by ANN. When compared with ANN, the MLPN model shows some significant advantages. Main results are presented in the form of five observations and supported by some experiments. |
| doi_str_mv | 10.1109/TSMCA.2009.2038068 |
| format | Article |
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Main results are presented in the form of five observations and supported by some experiments.</description><subject>Artificial neural networks</subject><subject>Computer architecture</subject><subject>Cybernetics</subject><subject>Fires</subject><subject>Fuzzy logic</subject><subject>Fuzzy sets</subject><subject>Fuzzy systems</subject><subject>Human</subject><subject>Knowledge base (KB)</subject><subject>Learning</subject><subject>Learning theory</subject><subject>Machine learning</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>Petri nets</subject><subject>Petri nets (PNs)</subject><subject>production rule</subject><subject>Remedies</subject><subject>Unsupervised learning</subject><issn>1083-4427</issn><issn>2168-2216</issn><issn>1558-2426</issn><issn>2168-2232</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp9kE1LAzEQhoMoWKt_QC-LF71snXwnRyl-Qf2AtueQ7GZlS7utya7Qf2_WFgUPXt4ZhucdZl6EzjGMMAZ9M5s-j29HBEAnoQqEOkADzLnKCSPiMPWgaM4YkcfoJMYFAGZMswFS027jw2cdfZnZpszmTfwdTLwNTd28Z26bzWPfvPk21NmLb-MpOqrsMvqzfR2i-f3dbPyYT14fnsa3k7ygXLc5F0UJvJTMJiXWawlMaK4rqjTYsnK-IESDs2AlKStSClKownknhJPOSTpEV7u9m7D-6HxszaqOhV8ubePXXTSSUclBcJbI639JLCQmCrTql17-QRfrLjTpD6O4pATSSQkiO6gI6xiDr8wm1CsbtgaD6VM336mbPnWzTz2ZLnam2nv_Y-BUc6E0_QKAbHzl</recordid><startdate>201003</startdate><enddate>201003</enddate><creator>Shen, V.</creator><creator>Yue-Shan Chang</creator><creator>Juang, T.T.-Y.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| ispartof | IEEE transactions on systems, man and cybernetics. Part A, Systems and humans, 2010-03, Vol.40 (2), p.363-375 |
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| language | eng |
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| source | IEEE Electronic Library Online |
| subjects | Artificial neural networks Computer architecture Cybernetics Fires Fuzzy logic Fuzzy sets Fuzzy systems Human Knowledge base (KB) Learning Learning theory Machine learning Neural networks Neurons Petri nets Petri nets (PNs) production rule Remedies Unsupervised learning |
| title | Supervised and Unsupervised Learning by Using Petri Nets |
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