Citation

Hiew, Bee Yan and Tan, Shing Chiang and Watada, Junzo (2023) A Coevolutioanary Neural Network for Detecting Chemical Gas Sensor Drift. In: Unconventional Methods for Geoscience, Shale Gas and Petroleum in the 21st Century. Advances in Energy Research and Development, 2 . IOS Press, pp. 219-228.

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Abstract

Sensor drift is a phenomenon which indicates unexpected variations in the sensory signal responses beneath the same working conditions. In this paper, a competitive co-evolutionary (ComCoE) Multilayer Perceptron artificial neural network (MLPN) is applied to detect chemical gas sensor drift. The efficiency of the ComCoE MLPN in detecting chemical gas sensor drift is evaluated as well as compared with the performance of other classification methods from the literature. The proposed ComCoE MLPN has shown promising preliminary results in this application

Item Type:	Book Section
Uncontrolled Keywords:	Coevolutionary, sensor drift, multilayer perceptron neural network
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	31 Oct 2023 00:56
Last Modified:	31 Oct 2023 00:56
URII:	http://shdl.mmu.edu.my/id/eprint/11758

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