Backpropagation, or propagation of error, is a common method of teaching artificial neural networks how to perform a given task. It was first described by Paul Werbos in 1974, but ...
http://en.wikipedia.org/wiki/Backpropagation

The Algorithm We want to train a multi-layer feedforward network by gradient descent to approximate an unknown function, based on some training data consisting of pairs (x,t).
http://www.willamette.edu/~gorr/classes/cs449/backprop.html

Backpropagation neural network training model, explanations and algorithms ... Multi-layer feed-forward networks; Delta rule; Understanding Backpropagation ; Working with ...
http://www.learnartificialneuralnetworks.com/backpropagation.html

Backpropagation is the basis for training a supervised neural network. Static backpropagation is used to produce an instantaneous mapping of a static (time independent) input to ...
http://www.nd.com/definitions/backprop.htm

Method Summary: double: getLearningRate () Get the learning rate associated with a Backpropagation method. double: getMomentum () Returns the momentum to be used as an ...
http://www.oracle.com/technology/products/bi/odm/JSR-73/javax/datamining/algorithm/feedforwardneuralnet/Backpropagation.html

This paper proposes a variation of the standard backpropagation BP algorithm that is particularly suitable for training neural networks utilized in multiresolution image ...
http://citeseer.ist.psu.edu/604325.html

P. J. Werbos. Backpropagation: Past and future. In Proc. of the ICNN-88, New York, pages 343--353, 1988.
http://citeseer.ist.psu.edu/context/196144/0

The Backpropagation Algorithm ... Furthermore, is the same regardless of which input weight of unit j we are trying to update.
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An Introduction to Back-Propagation Neural Networks. by Pete McCollum. Saipan59@juno.com. Introduction. This article focuses on a particular type of neural network model, known as ...
http://www.seattlerobotics.org/encoder/nov98/neural.html

This research examined the applicability of using a neural network approach to the estimation of aqueous activity coefficients of aromatic organic compounds from fragmented ...
http://dlist.sir.arizona.edu/503/

This is the standard type of Backpropagation network in which every layer is connected or linked to the immediately previous layer. NeuroShell 2 gives you the option of using a ...
http://www.wardsystems.com/manuals/neuroshell2/probackproparchstandard.htm

Hidden layers in a neural network are known as feature detectors. Ward Systems Group invented three different Backpropagation network architectures with multiple hidden layers for ...
http://www.wardsystems.com/manuals/neuroshell2/probackproparchward.htm

Overview. Backpropagation was created by generalizing the Widrow-Hoff learning rule to multiple-layer networks and nonlinear differentiable transfer functions.
http://www.mathworks.com/access/helpdesk_r13/help/toolbox/nnet/backpr5.html#32

There are essentially four steps when implementing backpropagation. 1. gathering training data 2. creating a net 3. training the net 4. simulating the net to new inputs
http://neuralnetworks.ai-depot.com/NeuralNetworks/1039.html

Neural Networks, Connectionist Systems, and Neural Systems areas/neural/systems/ am/ Aspirin/MIGRAINES: Neural Network Simulator (Backpropagation Networks) animator/ ...
http://www.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/neural/systems/0.html

This article demonstrates a backpropagation artificial neural network console application with validation and test sets for performance estimation using uneven distribution metrics ...
http://www.codeproject.com/Article.aspx?tag=0833430038913115

Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods (Genetic and Evolutionary Computation) by Nikolay Nikolaev and Hitoshi Iba ...
http://www.amazon.com/s?ie=UTF8&keywords=Backpropagation%20Learning&index=blended&page=1