An Interval Type-2 Neural Fuzzy System for Online System Identification and Feature Elimination
We propose an integrated mechanism for discarding derogatory features and extraction of fuzzy rules based on an interval type-2 neural fuzzy system (NFS)-in fact, it is a more general scheme that can discard bad features, irrelevant antecedent clauses, and even irrelevant rules. High-dimensional inp...
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| Veröffentlicht in: | IEEE transaction on neural networks and learning systems 2015-07, Vol.26 (7), p.1442-1455 |
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| creator | Chin-Teng Lin Pal, Nikhil R. Shang-Lin Wu Yu-Ting Liu Yang-Yin Lin |
| description | We propose an integrated mechanism for discarding derogatory features and extraction of fuzzy rules based on an interval type-2 neural fuzzy system (NFS)-in fact, it is a more general scheme that can discard bad features, irrelevant antecedent clauses, and even irrelevant rules. High-dimensional input variable and a large number of rules not only enhance the computational complexity of NFSs but also reduce their interpretability. Therefore, a mechanism for simultaneous extraction of fuzzy rules and reducing the impact of (or eliminating) the inferior features is necessary. The proposed approach, namely an interval type-2 Neural Fuzzy System for online System Identification and Feature Elimination (IT2NFS-SIFE), uses type-2 fuzzy sets to model uncertainties associated with information and data in designing the knowledge base. The consequent part of the IT2NFS-SIFE is of Takagi-Sugeno-Kang type with interval weights. The IT2NFS-SIFE possesses a self-evolving property that can automatically generate fuzzy rules. The poor features can be discarded through the concept of a membership modulator. The antecedent and modulator weights are learned using a gradient descent algorithm. The consequent part weights are tuned via the rule-ordered Kalman filter algorithm to enhance learning effectiveness. Simulation results show that IT2NFS-SIFE not only simplifies the system architecture by eliminating derogatory/irrelevant antecedent clauses, rules, and features but also maintains excellent performance. |
| doi_str_mv | 10.1109/TNNLS.2014.2346537 |
| format | Article |
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High-dimensional input variable and a large number of rules not only enhance the computational complexity of NFSs but also reduce their interpretability. Therefore, a mechanism for simultaneous extraction of fuzzy rules and reducing the impact of (or eliminating) the inferior features is necessary. The proposed approach, namely an interval type-2 Neural Fuzzy System for online System Identification and Feature Elimination (IT2NFS-SIFE), uses type-2 fuzzy sets to model uncertainties associated with information and data in designing the knowledge base. The consequent part of the IT2NFS-SIFE is of Takagi-Sugeno-Kang type with interval weights. The IT2NFS-SIFE possesses a self-evolving property that can automatically generate fuzzy rules. The poor features can be discarded through the concept of a membership modulator. The antecedent and modulator weights are learned using a gradient descent algorithm. The consequent part weights are tuned via the rule-ordered Kalman filter algorithm to enhance learning effectiveness. Simulation results show that IT2NFS-SIFE not only simplifies the system architecture by eliminating derogatory/irrelevant antecedent clauses, rules, and features but also maintains excellent performance.</description><identifier>ISSN: 2162-237X</identifier><identifier>EISSN: 2162-2388</identifier><identifier>DOI: 10.1109/TNNLS.2014.2346537</identifier><identifier>PMID: 25163074</identifier><identifier>CODEN: ITNNAL</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Algorithms ; Automobiles ; Chemical Industry ; Computer Simulation ; Feature extraction ; Feature selection ; Fuzzy Logic ; fuzzy neural network ; Fuzzy neural networks ; Fuzzy sets ; Input variables ; Kalman filters ; Machine Learning ; Modulation ; Neural Networks, Computer ; Normal Distribution ; online structure learning ; Online Systems ; system identification ; type-2 neural fuzzy systems (NFSs)</subject><ispartof>IEEE transaction on neural networks and learning systems, 2015-07, Vol.26 (7), p.1442-1455</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Jul 2015</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c465t-a24c736d2b2f00322772a5241f2462e115b480bd70851a76595b13865a4b14e63</citedby><cites>FETCH-LOGICAL-c465t-a24c736d2b2f00322772a5241f2462e115b480bd70851a76595b13865a4b14e63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6881716$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27923,27924,54757</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6881716$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25163074$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Chin-Teng Lin</creatorcontrib><creatorcontrib>Pal, Nikhil R.</creatorcontrib><creatorcontrib>Shang-Lin Wu</creatorcontrib><creatorcontrib>Yu-Ting Liu</creatorcontrib><creatorcontrib>Yang-Yin Lin</creatorcontrib><title>An Interval Type-2 Neural Fuzzy System for Online System Identification and Feature Elimination</title><title>IEEE transaction on neural networks and learning systems</title><addtitle>TNNLS</addtitle><addtitle>IEEE Trans Neural Netw Learn Syst</addtitle><description>We propose an integrated mechanism for discarding derogatory features and extraction of fuzzy rules based on an interval type-2 neural fuzzy system (NFS)-in fact, it is a more general scheme that can discard bad features, irrelevant antecedent clauses, and even irrelevant rules. High-dimensional input variable and a large number of rules not only enhance the computational complexity of NFSs but also reduce their interpretability. Therefore, a mechanism for simultaneous extraction of fuzzy rules and reducing the impact of (or eliminating) the inferior features is necessary. The proposed approach, namely an interval type-2 Neural Fuzzy System for online System Identification and Feature Elimination (IT2NFS-SIFE), uses type-2 fuzzy sets to model uncertainties associated with information and data in designing the knowledge base. The consequent part of the IT2NFS-SIFE is of Takagi-Sugeno-Kang type with interval weights. The IT2NFS-SIFE possesses a self-evolving property that can automatically generate fuzzy rules. The poor features can be discarded through the concept of a membership modulator. The antecedent and modulator weights are learned using a gradient descent algorithm. The consequent part weights are tuned via the rule-ordered Kalman filter algorithm to enhance learning effectiveness. Simulation results show that IT2NFS-SIFE not only simplifies the system architecture by eliminating derogatory/irrelevant antecedent clauses, rules, and features but also maintains excellent performance.</description><subject>Algorithms</subject><subject>Automobiles</subject><subject>Chemical Industry</subject><subject>Computer Simulation</subject><subject>Feature extraction</subject><subject>Feature selection</subject><subject>Fuzzy Logic</subject><subject>fuzzy neural network</subject><subject>Fuzzy neural networks</subject><subject>Fuzzy sets</subject><subject>Input variables</subject><subject>Kalman filters</subject><subject>Machine Learning</subject><subject>Modulation</subject><subject>Neural Networks, Computer</subject><subject>Normal Distribution</subject><subject>online structure learning</subject><subject>Online Systems</subject><subject>system identification</subject><subject>type-2 neural fuzzy systems (NFSs)</subject><issn>2162-237X</issn><issn>2162-2388</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNpdkU9r3DAQxU1oSUKaL9BCEfSSi7fS6K-PIWTThWVzyBZ6E7I9BgVb3kp2YfPpo2Q3e-hcNBr93vDQK4qvjC4Yo9XP7WazfloAZWIBXCjJ9VlxCUxBCdyYT6de_7korlN6prkUlUpU58UFSKY41eKysLeBrMKE8Z_ryXa_wxLIBueYb8v55WVPnvZpwoF0YySPofcBPyarFsPkO9-4yY-BuNCSJbppjkjuez_48D7_UnzuXJ_w-nheFb-X99u7X-X68WF1d7sum-x9Kh2IRnPVQg0dpRxAa3ASBOtAKEDGZC0MrVtNjWROK1nJmnGjpBM1E6j4VXFz2LuL498Z02QHnxrsexdwnJNlqqLANKtYRn_8hz6PcwzZXaZMZSg1XGQKDlQTx5QidnYX_eDi3jJq3xKw7wnYtwTsMYEs-n5cPdcDtifJx39n4NsB8Ih4elbGZGuKvwIgf4eT</recordid><startdate>20150701</startdate><enddate>20150701</enddate><creator>Chin-Teng Lin</creator><creator>Pal, Nikhil R.</creator><creator>Shang-Lin Wu</creator><creator>Yu-Ting Liu</creator><creator>Yang-Yin Lin</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Academic</collection><jtitle>IEEE transaction on neural networks and learning systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Chin-Teng Lin</au><au>Pal, Nikhil R.</au><au>Shang-Lin Wu</au><au>Yu-Ting Liu</au><au>Yang-Yin Lin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Interval Type-2 Neural Fuzzy System for Online System Identification and Feature Elimination</atitle><jtitle>IEEE transaction on neural networks and learning systems</jtitle><stitle>TNNLS</stitle><addtitle>IEEE Trans Neural Netw Learn Syst</addtitle><date>2015-07-01</date><risdate>2015</risdate><volume>26</volume><issue>7</issue><spage>1442</spage><epage>1455</epage><pages>1442-1455</pages><issn>2162-237X</issn><eissn>2162-2388</eissn><coden>ITNNAL</coden><abstract>We propose an integrated mechanism for discarding derogatory features and extraction of fuzzy rules based on an interval type-2 neural fuzzy system (NFS)-in fact, it is a more general scheme that can discard bad features, irrelevant antecedent clauses, and even irrelevant rules. High-dimensional input variable and a large number of rules not only enhance the computational complexity of NFSs but also reduce their interpretability. Therefore, a mechanism for simultaneous extraction of fuzzy rules and reducing the impact of (or eliminating) the inferior features is necessary. The proposed approach, namely an interval type-2 Neural Fuzzy System for online System Identification and Feature Elimination (IT2NFS-SIFE), uses type-2 fuzzy sets to model uncertainties associated with information and data in designing the knowledge base. The consequent part of the IT2NFS-SIFE is of Takagi-Sugeno-Kang type with interval weights. The IT2NFS-SIFE possesses a self-evolving property that can automatically generate fuzzy rules. The poor features can be discarded through the concept of a membership modulator. The antecedent and modulator weights are learned using a gradient descent algorithm. 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| subjects | Algorithms Automobiles Chemical Industry Computer Simulation Feature extraction Feature selection Fuzzy Logic fuzzy neural network Fuzzy neural networks Fuzzy sets Input variables Kalman filters Machine Learning Modulation Neural Networks, Computer Normal Distribution online structure learning Online Systems system identification type-2 neural fuzzy systems (NFSs) |
| title | An Interval Type-2 Neural Fuzzy System for Online System Identification and Feature Elimination |
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