Unification of neural and wavelet networks and fuzzy systems
Analyzes several commonly used soft computing paradigms (neural and wavelet networks and fuzzy systems, Bayesian classifiers, fuzzy partitions, etc.) and tries to outline similarities and differences among each other. These are exploited to produce the weighted radial basis functions paradigm which...
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| Veröffentlicht in: | IEEE transactions on neural networks 1999, Vol.10 (4), p.801-814 |
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| container_end_page | 814 |
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| container_title | IEEE transactions on neural networks |
| container_volume | 10 |
| creator | Reyneri, L.M. |
| description | Analyzes several commonly used soft computing paradigms (neural and wavelet networks and fuzzy systems, Bayesian classifiers, fuzzy partitions, etc.) and tries to outline similarities and differences among each other. These are exploited to produce the weighted radial basis functions paradigm which may act as a neuro-fuzzy unification paradigm. Training rules (both supervised and unsupervised) are also unified by the proposed algorithm. Analyzing differences and similarities among existing paradigms helps to understand that many soft computing paradigms are very similar to each other and can be grouped in just two major classes. The many reasons to unify soft computing paradigms are also shown in the paper. A conversion method is presented to convert perceptrons, radial basis functions, wavelet networks, and fuzzy systems from each other. |
| doi_str_mv | 10.1109/72.774224 |
| format | Article |
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These are exploited to produce the weighted radial basis functions paradigm which may act as a neuro-fuzzy unification paradigm. Training rules (both supervised and unsupervised) are also unified by the proposed algorithm. Analyzing differences and similarities among existing paradigms helps to understand that many soft computing paradigms are very similar to each other and can be grouped in just two major classes. The many reasons to unify soft computing paradigms are also shown in the paper. A conversion method is presented to convert perceptrons, radial basis functions, wavelet networks, and fuzzy systems from each other.</description><subject>Algorithms</subject><subject>Analogies</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Bayesian methods</subject><subject>Computer networks</subject><subject>Computer science; control theory; systems</subject><subject>Computers in experimental physics</subject><subject>Connectionism. Neural networks</subject><subject>Exact sciences and technology</subject><subject>Function approximation</subject><subject>Fuzzy neural networks</subject><subject>Fuzzy systems</subject><subject>Instruments, apparatus, components and techniques common to several branches of physics and astronomy</subject><subject>Learning</subject><subject>Learning and adaptive systems</subject><subject>Networks</subject><subject>Neural networks</subject><subject>Neural networks, fuzzy logic, artificial intelligence</subject><subject>Partitioning algorithms</subject><subject>Physics</subject><subject>Radial basis function</subject><subject>Soft computing</subject><subject>Taxonomy</subject><subject>Wavelet</subject><subject>Wavelet analysis</subject><issn>1045-9227</issn><issn>1941-0093</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1999</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqF0c1LwzAYBvAgipvTg1cP0oMoHjrz5qNpwIsMv2DgxZ1Llr6BatfOpnVsf73dWtSTnhLe_HjCy0PIKdAxANU3io2VEoyJPTIELSCkVPP99k6FDDVjakCOvH-jFISk0SEZQMwkk0oPye2syFxmTZ2VRVC6oMCmMnlgijRYmU_MsW5H9aqs3v1u6JrNZh34ta9x4Y_JgTO5x5P-HJHZw_3r5Cmcvjw-T-6moRVM1KGWmM4VSCu40KmzThvGQUm0NI21jKWQ6dylaAGlQhPFWqRRLOeW8sgKC3xErrrcZVV-NOjrZJF5i3luCiwbn2jQGhiT8b9Scd5uDpy38vJPyWLBQMXsf6gok9Eu8bqDtiq9r9AlyypbmGqdAE22PSWKJV1PrT3vQ5v5AtMf2RfTgoseGG9N7ipT2Mz_cioCsc0561iGiN-v_Sdf4gCgqg</recordid><startdate>1999</startdate><enddate>1999</enddate><creator>Reyneri, L.M.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7X8</scope><scope>7SP</scope><scope>F28</scope><scope>FR3</scope></search><sort><creationdate>1999</creationdate><title>Unification of neural and wavelet networks and fuzzy systems</title><author>Reyneri, L.M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c424t-95edb715c4349dfcf9a23175ec0d8958545dbfdec1e57ea6894d685bc036c4c13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1999</creationdate><topic>Algorithms</topic><topic>Analogies</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Artificial neural networks</topic><topic>Bayesian methods</topic><topic>Computer networks</topic><topic>Computer science; control theory; systems</topic><topic>Computers in experimental physics</topic><topic>Connectionism. Neural networks</topic><topic>Exact sciences and technology</topic><topic>Function approximation</topic><topic>Fuzzy neural networks</topic><topic>Fuzzy systems</topic><topic>Instruments, apparatus, components and techniques common to several branches of physics and astronomy</topic><topic>Learning</topic><topic>Learning and adaptive systems</topic><topic>Networks</topic><topic>Neural networks</topic><topic>Neural networks, fuzzy logic, artificial intelligence</topic><topic>Partitioning algorithms</topic><topic>Physics</topic><topic>Radial basis function</topic><topic>Soft computing</topic><topic>Taxonomy</topic><topic>Wavelet</topic><topic>Wavelet analysis</topic><toplevel>online_resources</toplevel><creatorcontrib>Reyneri, L.M.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Computer and Information Systems 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>MEDLINE - Academic</collection><collection>Electronics & Communications Abstracts</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on neural networks</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Reyneri, L.M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Unification of neural and wavelet networks and fuzzy systems</atitle><jtitle>IEEE transactions on neural networks</jtitle><stitle>TNN</stitle><addtitle>IEEE Trans Neural Netw</addtitle><date>1999</date><risdate>1999</risdate><volume>10</volume><issue>4</issue><spage>801</spage><epage>814</epage><pages>801-814</pages><issn>1045-9227</issn><eissn>1941-0093</eissn><coden>ITNNEP</coden><abstract>Analyzes several commonly used soft computing paradigms (neural and wavelet networks and fuzzy systems, Bayesian classifiers, fuzzy partitions, etc.) and tries to outline similarities and differences among each other. 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| identifier | ISSN: 1045-9227 |
| ispartof | IEEE transactions on neural networks, 1999, Vol.10 (4), p.801-814 |
| issn | 1045-9227 1941-0093 |
| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Analogies Applied sciences Artificial intelligence Artificial neural networks Bayesian methods Computer networks Computer science control theory systems Computers in experimental physics Connectionism. Neural networks Exact sciences and technology Function approximation Fuzzy neural networks Fuzzy systems Instruments, apparatus, components and techniques common to several branches of physics and astronomy Learning Learning and adaptive systems Networks Neural networks Neural networks, fuzzy logic, artificial intelligence Partitioning algorithms Physics Radial basis function Soft computing Taxonomy Wavelet Wavelet analysis |
| title | Unification of neural and wavelet networks and fuzzy systems |
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