Conjugate Gradient Trained Neural Network for Intelligent Sensing of Manhole Gases to Avoid Human Fatality

Introduction

The study of Computational Intelligence (CI) enabled us to formulate and/or model real life problems, especially for those problems where statistical modeling is inefficient or infeasible. The first and the primary component of CI is the Artificial Neural Network (ANN). ANN leads the field of CI. Later, bio-inspired computational methods, fuzzy logic and swarm intelligence are contributing their significant role in the field of CI. ANN, a powerful mathematical model inspired by the natural neural network system, is still occupying the central stage in CI. To some extent ANN, like natural neural networks, is able to solve highly complex nonlinear problems. The ANN, as an alternative, may be used for modeling those problems whose statistical modeling may not be erected. Our goal in the course of this chapter is to show the application of ANN and its modeling for a real life problem. For such cases, we have identified manhole gas discrimination/detection problem to be modeled using ANN. The manhole gas detection problem is a critical problem in the sense that, the pattern of gases influencing sensor used for sensing of gases is not only random but also dependent on change in the environment, such as temperature, humidity and pressure. The former is a major concern. The latter one can be exerted by employing supreme quality of sensors for sensing gases. In this chapter, we mention a design of an intelligent sensory system for sensing manhole gases, the design of which is as follows. The proposed intelligent system is having three modules, the first module, viz, the input module comprises gas chamber, the sensor array and data acquisition unit is dedicated for sensing the presence of gases in the manholes. Second module, the intelligent module is the ANN model which receives input from the first module and provides its response to the output module after processing the inputs. In the whole exercise the major tasks are a collection of data sample used in the training of the ANN model and the training of the ANN model itself. The ANN is trained in a supervised manner using the conjugate gradient method. Apart from the problem formulation, ANN training methodologies constitute the major subject to discuss through this chapter.