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- W1522851808 abstract "AbstractArtificial Neural Networks are inherently fault tolerant. Fault tolerance properties of artificial neural networks have been investigated with reference to the hardware model of artificial neural networks. Limited precision of neural hardware lead to study the sensitivity of feedforward layered networks for weight and input errors. In this paper, we analyze the sensitivity of feedforward layered network. We propose a framework for the investigation of fault tolerance properties of a hardware model of artificial neural networks. The result obtained indicates that networks obtained by training them with the resilient back propagation algorithm are not fault tolerant.KeywordsNeural NetworkArtificial Neural NetworkMean Square ErrorFault ToleranceHide NodeThese 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." @default.
- W1522851808 created "2016-06-24" @default.
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- W1522851808 date "2010-01-01" @default.
- W1522851808 modified "2023-09-22" @default.
- W1522851808 title "Sensitivity Measurement of Neural Hardware: A Simulation Based Study" @default.
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- W1522851808 doi "https://doi.org/10.1007/978-3-642-14825-5_12" @default.
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