Artificial Neural Networks Based SRGM

P. K. Kapur (), H. Pham (), A. Gupta () and P. C. Jha ()
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P. K. Kapur: University of Delhi
H. Pham: Rutgers University
A. Gupta: University of Delhi
P. C. Jha: University of Delhi

Chapter Chapter 7 in Software Reliability Assessment with OR Applications, 2011, pp 255-282 from Springer

Abstract: 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.

Keywords: Neural Network; Artificial Neural Network; Hide Layer; Activation Function; Output Layer (search for similar items in EconPapers)
Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-0-85729-204-9_7

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DOI: 10.1007/978-0-85729-204-9_7

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