Convergence Analysis of Backpropagation Algorithm for Designing an Intelligent System for Sensing Manhole Gases

TL;DR

This paper analyzes the convergence of the backpropagation algorithm when used to train neural networks for sensing hazardous gases in manholes, aiming to improve safety through intelligent detection systems.

Contribution

It provides a comprehensive study of backpropagation's performance on real-world gas sensing, comparing it with other hybrid approaches in both theoretical and statistical contexts.

Findings

Backpropagation effectively trains neural networks for gas detection.

Performance varies with sensor array complexity and gas mixture patterns.

Comparison shows hybrid approaches can enhance detection accuracy.

Abstract

Human fatalities are reported due to the excessive proportional presence of hazardous gas components in the manhole, such as Hydrogen Sulfide, Ammonia, Methane, Carbon Dioxide, Nitrogen Oxide, Carbon Monoxide, etc. Hence, predetermination of these gases is imperative. A neural network (NN) based intelligent sensory system is proposed for the avoidance of such fatalities. Backpropagation (BP) was applied for the supervised training of the neural network. A Gas sensor array consists of many sensor elements was employed for the sensing manhole gases. Sensors in the sensor array are responsible for sensing their target gas components only. Therefore, the presence of multiple gases results in cross sensitivity. The cross sensitivity is a crucial issue to this problem and it is viewed as pattern recognition and noise reduction problem. Various performance parameters and complexity of the problem influences NN training. In present chapter the performance of BP algorithm on such a real life application problem was comprehensively studied, compared and contrasted with the several other hybrid intelligent approaches both, in theoretical and in the statistical sense.