Matches in SemOpenAlex for { <https://semopenalex.org/work/W2162460982> ?p ?o ?g. }
Showing items 1 to 67 of
67
with 100 items per page.
- W2162460982 abstract "A new optimal strategy based on symbiotic modelling is proposed. The system combines Linear Regression Model (LR), Non-Linear Iterative Partial Adaptive Least Square Model (NIPALS), Neural Network Model with double loop procedures (NNDLP), Adaptive Numeric Modelling (Neural-Fuzzy modeling NF) and metallurgical knowledge in order to provide effective modelling solutions and achieve an optimal prediction performance. As a final step a fusion procedure is used to perform a routine decision making based on aggregation algorithm and clustering method that allow to systematically select the final best prediction outcome from a set of competing solutions. The proposed methodology is then applied to the challenging environment of a multi-dimensional, non-linear and sparse data space consisting of mechanical properties of `Mild' Steel in particular Tensile Strength (TS) and Yield Strength (YS) in hot-rolling industrial processes. Using a data set containing critical information on the mechanical properties obtained from a hot strip mill, it is concluded that the developed new systematic modelling approach is capable of providing better prediction than each individual model even in data distribution areas which are reckoned to be sparse." @default.
- W2162460982 created "2016-06-24" @default.
- W2162460982 creator A5032765391 @default.
- W2162460982 creator A5069916821 @default.
- W2162460982 creator A5051050779 @default.
- W2162460982 date "2010-07-01" @default.
- W2162460982 modified "2023-09-26" @default.
- W2162460982 title "‘Symbiotic’ data-driven modelling for the accurate prediction of mechanical properties of alloy steels" @default.
- W2162460982 cites W1512688160 @default.
- W2162460982 cites W1570834090 @default.
- W2162460982 cites W1972058192 @default.
- W2162460982 cites W1974826193 @default.
- W2162460982 cites W2002503728 @default.
- W2162460982 cites W2003187834 @default.
- W2162460982 cites W2019207321 @default.
- W2162460982 cites W2023796122 @default.
- W2162460982 cites W2032182327 @default.
- W2162460982 cites W2034163586 @default.
- W2162460982 cites W2041950600 @default.
- W2162460982 cites W2074529104 @default.
- W2162460982 cites W2086404107 @default.
- W2162460982 cites W2127218421 @default.
- W2162460982 cites W2241583403 @default.
- W2162460982 doi "https://doi.org/10.1109/is.2010.5548323" @default.
- W2162460982 hasPublicationYear "2010" @default.
- W2162460982 type Work @default.
- W2162460982 sameAs 2162460982 @default.
- W2162460982 citedByCount "3" @default.
- W2162460982 countsByYear W21624609822014 @default.
- W2162460982 countsByYear W21624609822017 @default.
- W2162460982 crossrefType "proceedings-article" @default.
- W2162460982 hasAuthorship W2162460982A5032765391 @default.
- W2162460982 hasAuthorship W2162460982A5051050779 @default.
- W2162460982 hasAuthorship W2162460982A5069916821 @default.
- W2162460982 hasConcept C124101348 @default.
- W2162460982 hasConcept C154945302 @default.
- W2162460982 hasConcept C177264268 @default.
- W2162460982 hasConcept C199360897 @default.
- W2162460982 hasConcept C41008148 @default.
- W2162460982 hasConcept C50644808 @default.
- W2162460982 hasConcept C58489278 @default.
- W2162460982 hasConcept C73555534 @default.
- W2162460982 hasConceptScore W2162460982C124101348 @default.
- W2162460982 hasConceptScore W2162460982C154945302 @default.
- W2162460982 hasConceptScore W2162460982C177264268 @default.
- W2162460982 hasConceptScore W2162460982C199360897 @default.
- W2162460982 hasConceptScore W2162460982C41008148 @default.
- W2162460982 hasConceptScore W2162460982C50644808 @default.
- W2162460982 hasConceptScore W2162460982C58489278 @default.
- W2162460982 hasConceptScore W2162460982C73555534 @default.
- W2162460982 hasLocation W21624609821 @default.
- W2162460982 hasOpenAccess W2162460982 @default.
- W2162460982 hasPrimaryLocation W21624609821 @default.
- W2162460982 hasRelatedWork W2125310307 @default.
- W2162460982 hasRelatedWork W2286998681 @default.
- W2162460982 hasRelatedWork W2366051640 @default.
- W2162460982 hasRelatedWork W2370909876 @default.
- W2162460982 hasRelatedWork W2786249788 @default.
- W2162460982 hasRelatedWork W2953411182 @default.
- W2162460982 hasRelatedWork W2991126413 @default.
- W2162460982 hasRelatedWork W3043816525 @default.
- W2162460982 hasRelatedWork W4205940116 @default.
- W2162460982 hasRelatedWork W2546768035 @default.
- W2162460982 isParatext "false" @default.
- W2162460982 isRetracted "false" @default.
- W2162460982 magId "2162460982" @default.
- W2162460982 workType "article" @default.