Performance Analysis Of Neuro Genetic Algorithm Applied On Detecting Proportion Of Components In Manhole Gas Mixture

TL;DR

This paper evaluates a neuro genetic algorithm's effectiveness in detecting and quantifying toxic gases in manhole gas mixtures using sensor arrays, addressing cross-sensitivity issues in pattern recognition.

Contribution

It introduces a real valued neuro genetic algorithm for multi-gas detection, enhancing pattern recognition in sensor data for toxic gas identification.

Findings

Neuro genetic algorithm improves detection accuracy.

Effective handling of sensor cross-sensitivity.

Enhanced pattern recognition in gas mixture analysis.

Abstract

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