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

  • Varun Kumar Ojha
  • Paramartha Dutta
  • Atal Chaudhuri
  • Hiranmay Saha
Part of the Studies in Computational Intelligence book series (SCI, volume 611)


Human fatalities are reported due to excessive proportional presence of hazardous gas components in manhole, such as hydrogen sulphide, ammonia, methane, carbon dioxide, nitrogen oxide, carbon monoxide, etc. Hence, predetermination of these gases is imperative. A neural network (NN)-based intelligent sensory system was proposed for avoiding such fatalities. Backpropagation (BP) was applied for supervised training of the proposed neural network model. A gas sensor array consists of many sensor elements was employed for sensing manhole gases. Sensors in the sensor array were only responsible for sensing their target gas components. Therefore, the presence of multiple gases results in cross sensitivity that is a crucial issue to this problem. It is viewed as a pattern recognition and noise reduction problem. Various performance parameters and complexity of the problem influences NN training. In this chapter, performance of BP algorithm on such real-life application was comprehensively studied, compared, and contrasted with several hybrid intelligent approache, both in theoretical and in statistical senses.


Gas detection Backpropagation Neural network Pattern recognition Parameter tuning Complexity analysis 



This work was supported by Department of Science & Technology (Govt. of India) for the financial supports vide Project No.: IDP/IND/02/2009 and the IPROCOM Marie Curie initial training network, funded through the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007–2013/ under REA grant agreement No. 316555.


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Copyright information

© Springer India 2016

Authors and Affiliations

  • Varun Kumar Ojha
    • 1
    • 2
  • Paramartha Dutta
    • 3
  • Atal Chaudhuri
    • 1
  • Hiranmay Saha
    • 4
  1. 1.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia
  2. 2.IT4InnovationsVŠB Technical University of OstravaOstravaCzech Republic
  3. 3.Department of Computer and System SciencesVisva-Bharati UniversitySantiniketanIndia
  4. 4.Centre of Excellence for Green Energy and Sensors SystemIndian Institute of Engineering Science and TechnologyHowrahIndia

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