Matches in SemOpenAlex for { <https://semopenalex.org/work/W2062917877> ?p ?o ?g. }
- W2062917877 endingPage "3122" @default.
- W2062917877 startingPage "3098" @default.
- W2062917877 abstract "Fuzzy rule derivation is often difficult and time-consuming, and requires expert knowledge. This creates a common bottleneck in fuzzy system design. In order to solve this problem, many fuzzy systems that automatically generate fuzzy rules from numerical data have been proposed. In this paper, we propose a fuzzy neural network based on mutual subsethood (MSBFNN) and its fuzzy rule identification algorithms. In our approach, fuzzy rules are described by different fuzzy sets. For each fuzzy set representing a fuzzy rule, the universe of discourse is defined as the summation of weighted membership grades of input linguistic terms that associate with the given fuzzy rule. In this manner, MSBFNN fully considers the contribution of input variables to the joint firing strength of fuzzy rules. Afterwards, the proposed fuzzy neural network quantifies the impacts of fuzzy rules on the consequent parts by fuzzy connections based on mutual subsethood. Furthermore, to enhance the knowledge representation and interpretation of the rules, a linear transformation from consequent parts to output is incorporated into MSBFNN so that higher accuracy can be achieved. In the parameter identification phase, the backpropagation algorithm is employed, and proper linear transformation is also determined dynamically. To demonstrate the capability of the MSBFNN, simulations in different areas including classification, regression and time series prediction are conducted. The proposed MSBFNN shows encouraging performance when benchmarked against other models." @default.
- W2062917877 created "2016-06-24" @default.
- W2062917877 creator A5039896971 @default.
- W2062917877 creator A5057170852 @default.
- W2062917877 creator A5057424890 @default.
- W2062917877 creator A5062087243 @default.
- W2062917877 creator A5087621349 @default.
- W2062917877 date "2009-08-01" @default.
- W2062917877 modified "2023-09-28" @default.
- W2062917877 title "A fuzzy neural network with fuzzy impact grades" @default.
- W2062917877 cites W120161273 @default.
- W2062917877 cites W1586249796 @default.
- W2062917877 cites W1974904239 @default.
- W2062917877 cites W1975936518 @default.
- W2062917877 cites W1976070030 @default.
- W2062917877 cites W1985541966 @default.
- W2062917877 cites W2001255626 @default.
- W2062917877 cites W2001619934 @default.
- W2062917877 cites W2007220912 @default.
- W2062917877 cites W2019207321 @default.
- W2062917877 cites W2021347076 @default.
- W2062917877 cites W2024822381 @default.
- W2062917877 cites W2033194777 @default.
- W2062917877 cites W2043077493 @default.
- W2062917877 cites W2048819046 @default.
- W2062917877 cites W2050713825 @default.
- W2062917877 cites W2060584878 @default.
- W2062917877 cites W2062706881 @default.
- W2062917877 cites W2062746990 @default.
- W2062917877 cites W2063527411 @default.
- W2062917877 cites W2073782729 @default.
- W2062917877 cites W2075234193 @default.
- W2062917877 cites W2078094465 @default.
- W2062917877 cites W2079211475 @default.
- W2062917877 cites W2079325629 @default.
- W2062917877 cites W2085629944 @default.
- W2062917877 cites W2100110464 @default.
- W2062917877 cites W2102675003 @default.
- W2062917877 cites W2112036221 @default.
- W2062917877 cites W2114883321 @default.
- W2062917877 cites W2116841002 @default.
- W2062917877 cites W2116896485 @default.
- W2062917877 cites W2118790915 @default.
- W2062917877 cites W2120141422 @default.
- W2062917877 cites W2121731219 @default.
- W2062917877 cites W2130913096 @default.
- W2062917877 cites W2131054889 @default.
- W2062917877 cites W2132491934 @default.
- W2062917877 cites W2132636796 @default.
- W2062917877 cites W2133135175 @default.
- W2062917877 cites W2134620325 @default.
- W2062917877 cites W2134961275 @default.
- W2062917877 cites W2135467229 @default.
- W2062917877 cites W2137193994 @default.
- W2062917877 cites W2137525015 @default.
- W2062917877 cites W2138107248 @default.
- W2062917877 cites W2142137407 @default.
- W2062917877 cites W2142148616 @default.
- W2062917877 cites W2144397661 @default.
- W2062917877 cites W2144534900 @default.
- W2062917877 cites W2147684166 @default.
- W2062917877 cites W2152529194 @default.
- W2062917877 cites W2155399784 @default.
- W2062917877 cites W2156060469 @default.
- W2062917877 cites W2156222099 @default.
- W2062917877 cites W2159111290 @default.
- W2062917877 cites W2159749003 @default.
- W2062917877 cites W2161946664 @default.
- W2062917877 cites W2162447870 @default.
- W2062917877 cites W2162635690 @default.
- W2062917877 cites W2165729498 @default.
- W2062917877 cites W2169814901 @default.
- W2062917877 cites W2259289169 @default.
- W2062917877 cites W2622105632 @default.
- W2062917877 cites W3017143921 @default.
- W2062917877 cites W3127461809 @default.
- W2062917877 cites W47126999 @default.
- W2062917877 cites W2182702606 @default.
- W2062917877 doi "https://doi.org/10.1016/j.neucom.2009.03.009" @default.
- W2062917877 hasPublicationYear "2009" @default.
- W2062917877 type Work @default.
- W2062917877 sameAs 2062917877 @default.
- W2062917877 citedByCount "32" @default.
- W2062917877 countsByYear W20629178772012 @default.
- W2062917877 countsByYear W20629178772013 @default.
- W2062917877 countsByYear W20629178772014 @default.
- W2062917877 countsByYear W20629178772015 @default.
- W2062917877 countsByYear W20629178772016 @default.
- W2062917877 countsByYear W20629178772017 @default.
- W2062917877 countsByYear W20629178772019 @default.
- W2062917877 countsByYear W20629178772020 @default.
- W2062917877 crossrefType "journal-article" @default.
- W2062917877 hasAuthorship W2062917877A5039896971 @default.
- W2062917877 hasAuthorship W2062917877A5057170852 @default.
- W2062917877 hasAuthorship W2062917877A5057424890 @default.
- W2062917877 hasAuthorship W2062917877A5062087243 @default.
- W2062917877 hasAuthorship W2062917877A5087621349 @default.
- W2062917877 hasConcept C119857082 @default.