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Knock detection based on neural networks [articol] /

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dc.contributor.author Lăzărescu, Dan
dc.contributor.author Ungureanu, Mihaela G.
dc.date.accessioned 2021-09-09T10:08:46Z
dc.date.available 2021-09-09T10:08:46Z
dc.date.issued 2004
dc.identifier.citation Lăzărescu, Dan. Knock detection based on neural networks. Timişoara: Editura Politehnica, 2004 en_US
dc.identifier.uri http://primo.upt.ro:1701/primo-explore/search?query=any,contains,Knock%20detection%20based%20on%20neural%20networks&tab=default_tab&search_scope=40TUT&vid=40TUT_V1&lang=ro_RO&offset=0 Link Primo
dc.description.abstract The paper presents a new method for knock detection based on two neural networks. First a discrete Hopfield network extracts features from the structural vibration signal. Then the first and the forth coefficients of the autoregressive model and the maximal and minimal value of the signal are applied to a feedforward neural network in order to detect the knock. Once a cycle has been detected as a knock containing one, for the next cycle the engine can be protected in order to avoid further appearances of knock. For the feedforward neural network it was experimentally determined that four neurons in the hidden layer is the best solution for the knock detection. en_US
dc.language.iso en en_US
dc.publisher Timişoara : Editura Politehnica en_US
dc.relation.ispartofseries Buletinul ştiinţific al Universităţii „Politehnica” din Timişoara, România. Seria electronică şi telecomunicaţii, Tom 49(63), fasc. 1 (2004), p. 253-255
dc.subject Neural networks en_US
dc.subject Features extraction en_US
dc.subject Hopfield algorithm en_US
dc.subject Knock detection en_US
dc.title Knock detection based on neural networks [articol] / en_US
dc.type Article en_US


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