Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0378-7796(96)01063-2
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dc.titleSimulated hardware design of artificial neural networks for adaptive plant control
dc.contributor.authorChang, C.S.
dc.contributor.authorWang, F.
dc.contributor.authorLiew, A.C.
dc.date.accessioned2014-10-07T03:05:26Z
dc.date.available2014-10-07T03:05:26Z
dc.date.issued1996-06
dc.identifier.citationChang, C.S., Wang, F., Liew, A.C. (1996-06). Simulated hardware design of artificial neural networks for adaptive plant control. Electric Power Systems Research 37 (3) : 231-240. ScholarBank@NUS Repository. https://doi.org/10.1016/S0378-7796(96)01063-2
dc.identifier.issn03787796
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/81172
dc.description.abstractIn this paper, an artificial neural network (ANN) hardware circuit design for implementing online plant parameter identification and plant control is presented. The parallel structure of the ANN hardware is typical of the feedforward network with a real-time back-propagation training algorithm. The circuit is designed for implementing energy function minimization and the gradient descent algorithm. Different schemes of the hardware design are discussed for realizing adaptive control functions. Simulated results show that the proposed ANN circuit design has fulfilled the performance objective as required.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/S0378-7796(96)01063-2
dc.sourceScopus
dc.subjectArtificial neural networks
dc.subjectController design
dc.subjectHardware circuit design
dc.typeArticle
dc.contributor.departmentELECTRICAL ENGINEERING
dc.description.doi10.1016/S0378-7796(96)01063-2
dc.description.sourcetitleElectric Power Systems Research
dc.description.volume37
dc.description.issue3
dc.description.page231-240
dc.description.codenEPSRD
dc.identifier.isiutA1996VZ90100009
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