Abstract
An optimized fuzzy inference system for carbon monoxide and methane concentration estimation is presented and compared to the three most common linear methods: PLS, PCR and MLR, and also to nonlinear extensions of PLS. The system optimization includes: rule pruning, membership function optimization by Solis–Wett algorithm, rule consequents optimization and sensor selection by sequential floating feature selection (SFFS) algorithm. An extensive data set obtained from a sensor array composed of five metal oxide gas sensors operated at two working temperatures in different humidity conditions is used for the method evaluation. Advantages and drawbacks of both linear methods and fuzzy systems are discussed and compared.
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