Artificial Neural Networks Based SRGM

  • P. K. Kapur
  • H. Pham
  • A. Gupta
  • P. C. Jha
Part of the Springer Series in Reliability Engineering book series (RELIABILITY)


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.


Neural Network Artificial Neural Network Hide Layer Activation Function Output Layer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

©  Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Department of Operational ResearchUniversity of DelhiDelhiIndia
  2. 2.Department of Industrial and Systems EngineeringRutgers UniversityPiscatawayUSA

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