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

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Hybrid Soft Computing Approaches

Part of the book series: Studies in Computational Intelligence ((SCI,volume 611))

Abstract

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.

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Acknowledgments

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.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Varun Kumar Ojha & Atal Chaudhuri

  2. IT4Innovations, VŠB Technical University of Ostrava, Ostrava, Czech Republic

    Varun Kumar Ojha

  3. Department of Computer and System Sciences, Visva-Bharati University, Santiniketan, India

    Paramartha Dutta

  4. Centre of Excellence for Green Energy and Sensors System, Indian Institute of Engineering Science and Technology, Howrah, India

    Hiranmay Saha

Authors
  1. Varun Kumar Ojha
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  2. Paramartha Dutta
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  3. Atal Chaudhuri
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  4. Hiranmay Saha
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Corresponding author

Correspondence to Varun Kumar Ojha .

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Editors and Affiliations

  1. Department of Information Technology, RCC Institute of Information Technology, Kolkata, West Bengal, India

    Siddhartha Bhattacharyya

  2. Department of Computer and System Sciences, Visva-Bharati University, Santiniketan, West Bengal, India

    Paramartha Dutta

  3. Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Howrah, India

    Susanta Chakraborty

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Ojha, V.K., Dutta, P., Chaudhuri, A., Saha, H. (2016). Convergence Analysis of Backpropagation Algorithm for Designing an Intelligent System for Sensing Manhole Gases. In: Bhattacharyya, S., Dutta, P., Chakraborty, S. (eds) Hybrid Soft Computing Approaches. Studies in Computational Intelligence, vol 611. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2544-7_7

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  • DOI: https://doi.org/10.1007/978-81-322-2544-7_7

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