Journal ArticleDOI
Learning representations by back-propagating errors
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TLDR
Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.Abstract:
We describe a new learning procedure, back-propagation, for networks of neurone-like units. The procedure repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector. As a result of the weight adjustments, internal ‘hidden’ units which are not part of the input or output come to represent important features of the task domain, and the regularities in the task are captured by the interactions of these units. The ability to create useful new features distinguishes back-propagation from earlier, simpler methods such as the perceptron-convergence procedure1.read more
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Journal ArticleDOI
A survey on Deep Learning based bearing fault diagnosis
Duy-Tang Hoang,Hee-Jun Kang +1 more
TL;DR: The three popular Deep Learning algorithms for Bearing fault diagnosis including Autoencoder, Restricted Boltzmann Machine, and Convolutional Neural Network are briefly introduced and their applications are reviewed through publications and research works on the area of bearing fault diagnosis.
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Patent
System and method for detecting fraudulent transactions
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Journal ArticleDOI
Learning in spiking neural networks by reinforcement of stochastic synaptic transmission.
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Journal ArticleDOI
Stochastic Configuration Networks: Fundamentals and Algorithms
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