FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2064752705> ?p ?o ?g. }

• W2064752705 endingPage "608" @default.
• W2064752705 startingPage "604" @default.
• W2064752705 abstract "In this paper, an application of dynamic neuro-fuzzy systems is presented for modeling the subsystems of the heat recovery steam generator (HRSG). The dynamic neuro-fuzzy models were developed based on the formal NARX models topology. The clustering techniques were employed to define the structure of the fuzzy models by dividing the entire operating regions into smaller subspaces. The optimal cluster centers and corresponding membership functions are captured by FCM, where the parameters of consequent were adjusted by recursive LSE method. A comparison between the responses of the proposed models and the responses of the plants ware preformed, which validates the accuracy and performance of the modeling approach. represented in a network structure. The learning techniques for neural network can be applied in order to tune the parameters of the fuzzy models (8). In ANIFS structure presented by Jang (1999), the number of fuzzy rules is equal to the product of number of membership functions and the number of inputs (9). In some cases, the required number of fuzzy rules to cover entire input spaces is very large, which causes the training process becomes time consuming or practically impossible. In order to reduce the number of fuzzy rules without accuracy losses, the fuzzy c-means (FCM) clustering approach was proposed to define the structure of fuzzy systems (10). In this paper, a combination of fuzzy c-means clustering and least square techniques are employed to identify the parameters of membership functions and fuzzy rules in a multi-input single-output (MISO) TSK type fuzzy inference systems (FIS). The FCM clustering is first employed to extract the number of fuzzy rules and membership functions for the antecedents. Then, the parameters of consequents are defined for model based on a given set of input/output data. The accuracy of developed models is validated by performing a comparison between the responses of developed models and the experimental data. In next section, a brief description of the plant that consists of a general view of the power plant and its subsystems is presented. Inputs and outputs to the subsystems are also specified in this section. The neuro-fuzzy model based on the experimental data and structure of recurrent model and simulation result is presented in Section IV. In addition, a comparison between the responses of the proposed models with the responses of the real plant is presented to validate the accuracy of the developed models." @default.
• W2064752705 created "2016-06-24" @default.
• W2064752705 creator A5045252533 @default.
• W2064752705 creator A5049887498 @default.
• W2064752705 creator A5078040199 @default.
• W2064752705 date "2012-01-01" @default.
• W2064752705 modified "2023-10-14" @default.
• W2064752705 title "Neuro-Fuzzy Modeling of Heat Recovery Steam Generator" @default.
• W2064752705 cites W1599678989 @default.
• W2064752705 cites W1969565635 @default.
• W2064752705 cites W2009271641 @default.
• W2064752705 cites W2013687186 @default.
• W2064752705 cites W2019207321 @default.
• W2064752705 cites W2036389364 @default.
• W2064752705 cites W2045732544 @default.
• W2064752705 cites W2067996109 @default.
• W2064752705 cites W2095402443 @default.
• W2064752705 cites W2129645442 @default.
• W2064752705 cites W2154743091 @default.
• W2064752705 doi "https://doi.org/10.7763/ijmlc.2012.v2.198" @default.
• W2064752705 hasPublicationYear "2012" @default.
• W2064752705 type Work @default.
• W2064752705 sameAs 2064752705 @default.
• W2064752705 citedByCount "4" @default.
• W2064752705 countsByYear W20647527052014 @default.
• W2064752705 countsByYear W20647527052017 @default.
• W2064752705 countsByYear W20647527052020 @default.
• W2064752705 countsByYear W20647527052021 @default.
• W2064752705 crossrefType "journal-article" @default.
• W2064752705 hasAuthorship W2064752705A5045252533 @default.
• W2064752705 hasAuthorship W2064752705A5049887498 @default.
• W2064752705 hasAuthorship W2064752705A5078040199 @default.
• W2064752705 hasBestOaLocation W20647527051 @default.
• W2064752705 hasConcept C121332964 @default.
• W2064752705 hasConcept C154945302 @default.
• W2064752705 hasConcept C163258240 @default.
• W2064752705 hasConcept C2780013297 @default.
• W2064752705 hasConcept C2780992000 @default.
• W2064752705 hasConcept C41008148 @default.
• W2064752705 hasConcept C58166 @default.
• W2064752705 hasConcept C97355855 @default.
• W2064752705 hasConceptScore W2064752705C121332964 @default.
• W2064752705 hasConceptScore W2064752705C154945302 @default.
• W2064752705 hasConceptScore W2064752705C163258240 @default.
• W2064752705 hasConceptScore W2064752705C2780013297 @default.
• W2064752705 hasConceptScore W2064752705C2780992000 @default.
• W2064752705 hasConceptScore W2064752705C41008148 @default.
• W2064752705 hasConceptScore W2064752705C58166 @default.
• W2064752705 hasConceptScore W2064752705C97355855 @default.
• W2064752705 hasLocation W20647527051 @default.
• W2064752705 hasOpenAccess W2064752705 @default.
• W2064752705 hasPrimaryLocation W20647527051 @default.
• W2064752705 hasRelatedWork W2000422883 @default.
• W2064752705 hasRelatedWork W2099634336 @default.
• W2064752705 hasRelatedWork W2356629573 @default.
• W2064752705 hasRelatedWork W2375354128 @default.
• W2064752705 hasRelatedWork W2390459957 @default.
• W2064752705 hasRelatedWork W2738546080 @default.
• W2064752705 hasRelatedWork W2746742710 @default.
• W2064752705 hasRelatedWork W3200756997 @default.
• W2064752705 hasRelatedWork W4311909815 @default.
• W2064752705 hasRelatedWork W108004069 @default.
• W2064752705 isParatext "false" @default.
• W2064752705 isRetracted "false" @default.
• W2064752705 magId "2064752705" @default.
• W2064752705 workType "article" @default.