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Performance Analysis of Neuro Genetic Algorithm Applied on Detecting Proportion of Components in Manhole Gas Mixture

Keywords: Gas mixture , Gas sensor array , Cross-sensitivity , Floating point , Genetic algorithm , Neural network , Optimization , Computational complexity

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The article presents performance analysis of a real valued neuro genetic algorithm applied for thedetection of proportion of the gases found in manhole gas mixture. The neural network (NN) trained usinggenetic algorithm (GA) leads to concept of neuro genetic algorithm, which is used for implementing anintelligent sensory system for the detection of component gases present in manhole gas mixture Usually amanhole gas mixture contains several toxic gases like Hydrogen Sulfide, Ammonia, Methane, CarbonDioxide, Nitrogen Oxide, and Carbon Monoxide. A semiconductor based gas sensor array used for sensingmanhole gas components is an integral part of the proposed intelligent system. It consists of many sensorelements, where each sensor element is responsible for sensing particular gas component. Multiple sensorsof different gases used for detecting gas mixture of multiple gases, results in cross-sensitivity. The crosssensitivity is a major issue and the problem is viewed as pattern recognition problem. The objective of thisarticle is to present performance analysis of the real valued neuro genetic algorithm which is applied formultiple gas detection.


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