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A Coevolutioanary Neural Network for Detecting Chemical Gas Sensor Drift
Bee Yan Hiew, Shing Chiang Tan, Junzo Watada
219 - 228
10.3233/AERD230020
Research Article
Advances in Energy Research and Development
Volume 2: Unconventional Methods for Geoscience, Shale Gas and Petroleum in the 21st Century
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.

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