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Intelligent Communication, Control and Devices pp 333–344Cite as

Optimization of DC-DC Converters for Off-Grid Lighting in Trains Using Artificial Neural Networks

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 989))

Abstract

The paper manifests the optimization of DC-DC converters for off-grid lighting in trains with Artificial Neural Networks using distinct soft computing techniques as Hopfield, recurrent networks, vector quantization, cascade forward and Elman backpropagation techniques. These techniques are user-friendly and give optimum results in less time with minimum errors.

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Author information

Authors and Affiliations

  1. Department of Electronics and Electrical Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India

    Monu Malik

  2. Department of Electrical Engineering, National Institute of Technology, Kurukshetra, India

    Ratna Dahiya

Authors
  1. Monu Malik
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  2. Ratna Dahiya
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Corresponding author

Correspondence to Monu Malik .

Editor information

Editors and Affiliations

  1. Department of Electronics, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    Sushabhan Choudhury

  2. Department of Electronics, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    Ranjan Mishra

  3. Department of Electronics, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    Raj Gaurav Mishra

  4. Department of Electronics, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    Adesh Kumar

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Malik, M., Dahiya, R. (2020). Optimization of DC-DC Converters for Off-Grid Lighting in Trains Using Artificial Neural Networks. In: Choudhury, S., Mishra, R., Mishra, R., Kumar, A. (eds) Intelligent Communication, Control and Devices. Advances in Intelligent Systems and Computing, vol 989. Springer, Singapore. https://doi.org/10.1007/978-981-13-8618-3_36

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