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Artificial Neural Networks Based SRGM

In: Software Reliability Assessment with OR Applications

Author

Listed:
  • P. K. Kapur

    (University of Delhi)

  • H. Pham

    (Rutgers University)

  • A. Gupta

    (University of Delhi)

  • P. C. Jha

    (University of Delhi)

Abstract

An Artificial Neural Network (ANN) is a computational paradigm that is inspired by the behavior of biological nervous systems, such as the brain, to process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems capable of revealing complex global behavior, determined by the connections between the processing elements and element parameters. ANN, like people, learns by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. In more practical terms neural networks are non-linear statistical data modeling or decision-making tools. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of ANN as well.

Suggested Citation

  • P. K. Kapur & H. Pham & A. Gupta & P. C. Jha, 2011. "Artificial Neural Networks Based SRGM," Springer Series in Reliability Engineering, in: Software Reliability Assessment with OR Applications, chapter 0, pages 255-282, Springer.
    • Handle: RePEc:spr:ssrchp:978-0-85729-204-9_7
    DOI: 10.1007/978-0-85729-204-9_7
    as

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