Matches in SemOpenAlex for { <https://semopenalex.org/work/W2912693001> ?p ?o ?g. }
Showing items 1 to 55 of
55
with 100 items per page.
- W2912693001 abstract "This dissertation investigates the applicability of artificial neural network systems to preliminary engineering design tasks. Synthesizing new, possibly innovative designs by exploring the development of structural topologies and determining their possible behaviors are two steps of preliminary design where this research concentrates. These two areas of preliminary structural design have proven difficult for design researchers. Using the neural network approach toward these tasks is feasible, but issues such as representing design problems in neural networks, collecting good design examples, and measuring network performance are still unresolved.This research begins by examining philosophies of design, which provides a basis for later discussions. In particular, the influence of design automation and computational models of design processes on the science of design are considered.Next, this work provides an introduction to artificial neural networks. Two classes of neural models, constraint satisfaction and supervised learning models, are examined in depth. The constraint satisfaction model is later used for development of a system for qualitative evaluation of preliminary designs. Supervised learning models provide the cornerstone for development of a model that uses induction in an attempt to learn from design examples, generalize results, and generate preliminary structural designs.A major bottleneck in developing most knowledge based systems is acquiring and representing requisite knowledge. Supervised learning models of connectionism have the potential to alleviate this obstacle. The second neural network system discussed and demonstrated is a hybrid back propagation model. This system can learn from examples of previous designs and is able to generate new designs.In addition to design issues, the discussion of connectionist models includes details of the different models, their performance, attributes, integrity, and shortcomings. The results of this research are an initial investigation into connectionism as applied to design. Both connectionism and the theory of design are relatively young in terms of formal research when compared to traditional areas of engineering and science. This work contributes to the maturing effort and identifies promising areas for further research." @default.
- W2912693001 created "2019-02-21" @default.
- W2912693001 creator A5000130424 @default.
- W2912693001 creator A5017076401 @default.
- W2912693001 date "1995-01-01" @default.
- W2912693001 modified "2023-09-23" @default.
- W2912693001 title "Automated preliminary design using artificial neural networks" @default.
- W2912693001 hasPublicationYear "1995" @default.
- W2912693001 type Work @default.
- W2912693001 sameAs 2912693001 @default.
- W2912693001 citedByCount "0" @default.
- W2912693001 crossrefType "journal-article" @default.
- W2912693001 hasAuthorship W2912693001A5000130424 @default.
- W2912693001 hasAuthorship W2912693001A5017076401 @default.
- W2912693001 hasConcept C119857082 @default.
- W2912693001 hasConcept C149635348 @default.
- W2912693001 hasConcept C154945302 @default.
- W2912693001 hasConcept C2780513914 @default.
- W2912693001 hasConcept C41008148 @default.
- W2912693001 hasConcept C50644808 @default.
- W2912693001 hasConcept C8521452 @default.
- W2912693001 hasConceptScore W2912693001C119857082 @default.
- W2912693001 hasConceptScore W2912693001C149635348 @default.
- W2912693001 hasConceptScore W2912693001C154945302 @default.
- W2912693001 hasConceptScore W2912693001C2780513914 @default.
- W2912693001 hasConceptScore W2912693001C41008148 @default.
- W2912693001 hasConceptScore W2912693001C50644808 @default.
- W2912693001 hasConceptScore W2912693001C8521452 @default.
- W2912693001 hasLocation W29126930011 @default.
- W2912693001 hasOpenAccess W2912693001 @default.
- W2912693001 hasPrimaryLocation W29126930011 @default.
- W2912693001 hasRelatedWork W1149494683 @default.
- W2912693001 hasRelatedWork W1539860474 @default.
- W2912693001 hasRelatedWork W1548941084 @default.
- W2912693001 hasRelatedWork W1567658875 @default.
- W2912693001 hasRelatedWork W1965229818 @default.
- W2912693001 hasRelatedWork W2017149666 @default.
- W2912693001 hasRelatedWork W2020246210 @default.
- W2912693001 hasRelatedWork W2049399010 @default.
- W2912693001 hasRelatedWork W2135322281 @default.
- W2912693001 hasRelatedWork W2273352767 @default.
- W2912693001 hasRelatedWork W2739331704 @default.
- W2912693001 hasRelatedWork W2912931692 @default.
- W2912693001 hasRelatedWork W3001302878 @default.
- W2912693001 hasRelatedWork W3033959096 @default.
- W2912693001 hasRelatedWork W3093138203 @default.
- W2912693001 hasRelatedWork W3164008977 @default.
- W2912693001 hasRelatedWork W58949042 @default.
- W2912693001 hasRelatedWork W2268798980 @default.
- W2912693001 hasRelatedWork W2784976645 @default.
- W2912693001 hasRelatedWork W3164835942 @default.
- W2912693001 isParatext "false" @default.
- W2912693001 isRetracted "false" @default.
- W2912693001 magId "2912693001" @default.
- W2912693001 workType "article" @default.