Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/104574
DC FieldValue
dc.titleImprecise reasoning using neural networks
dc.contributor.authorHsu, Loke-Soo
dc.contributor.authorTeh, Hoon-Heng
dc.contributor.authorChan, Sing-Chai
dc.contributor.authorLoe, Kia Fock
dc.date.accessioned2014-10-28T02:51:02Z
dc.date.available2014-10-28T02:51:02Z
dc.date.issued1990
dc.identifier.citationHsu, Loke-Soo,Teh, Hoon-Heng,Chan, Sing-Chai,Loe, Kia Fock (1990). Imprecise reasoning using neural networks. Proceedings of the Hawaii International Conference on System Science 4 : 363-368. ScholarBank@NUS Repository.
dc.identifier.isbn0818620110
dc.identifier.issn00731129
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/104574
dc.description.abstractA logic is defined that weighs all available information and implements it using an emulated neural network. This allows the resulting expert system to be able to learn through examples. It also handles fuzziness in the facts and the rules, as well as the logical operations, in a natural and uniform way. It is more realistic than the certainty factor formalism which leaves out information because it takes the minimum of the certainty factors for the AND operation and maximum of the certainty factors for the OR operation. In the present scheme, all activations are weighted and taken into account. Compared with classical expert systems, the present system has the advantage of operating in two modes. In the normal mode, rules are given by experts and weights are assigned values. In the learning mode, weights are allowed to vary while the system is fed with examples.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentINFORMATION SYSTEMS & COMPUTER SCIENCE
dc.contributor.departmentMATHEMATICS
dc.description.sourcetitleProceedings of the Hawaii International Conference on System Science
dc.description.volume4
dc.description.page363-368
dc.description.codenPHISD
dc.identifier.isiutNOT_IN_WOS
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