Results 251 to 260 of about 129,225 (287)

Out-of-core backpropagation

1990 IJCNN International Joint Conference on Neural Networks, 1990
Backpropagation learning can execute at supercomputer speed from training data sets of unprecedented size when supercomputer main memory is backed with newly available parallel arrays of commodity disk drives. An efficient implementation of backpropagation learning was modified and extended to iterate through training data sets stored on a parallel ...
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A fuzzy backpropagation algorithm

Fuzzy Sets and Systems, 2000
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Stefka Stoeva, Alexander Nikov
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Modelling with constructive backpropagation

Neural Networks, 1999
Neural network methods have proven to be powerful tools in modelling of nonlinear processes. One crucial part of modelling is the training phase where the model parameters are adjusted so that the model performs the desired operation as well as possible. Besides parameter estimation, an important problem is to select a suitable model structure.
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Backpropagation

2023
Christopher M. Bishop, Hugh Bishop
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Variational Foundations of Online Backpropagation

2013
On-line Backpropagation has become very popular and it has been the subject of in-depth theoretical analyses and massive experimentation. Yet, after almost three decades from its publication, it is still surprisingly the source of tough theoretical questions and of experimental results that are somewhat shrouded in mystery.
Frandina S., Gori M., Lippi M., Maggini M., Melacci S.   +4 more
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Strictly local backpropagation

1990 IJCNN International Joint Conference on Neural Networks, 1990
In conventional implementations of the backpropagation method, the delta error terms which propagate the error back through the network are global (common) variables shared by multiple nodes. A modified implementation of the algorithm which permits this process to occur using only local values of all variables is presented.
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Backpropagation

2022
Robert H. Chen, Chelsea Chen
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From Backpropagation to Neurocontrol

2011
This chapter provides an overview of the most powerful practical tools developed so far, and under development, in the areas which the Engineering Directorate of National Science Foundation has called “cognitive optimization and prediction”. It deals with a condensed overview of key tools and discusses the historical background and the larger ...
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Generalization of backpropagation with application to a recurrent gas market model

Neural Networks, 1988
Paul J Werbos
exaly  

Noise-boosted bidirectional backpropagation and adversarial learning

Neural Networks, 2019
Olaoluwa Adigun, Bart Kosko
exaly