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Backpropagation

Known as: Error back-propagation, Backpropogation, Back prop 
Backpropagation, an abbreviation for "backward propagation of errors", is a common method of training artificial neural networks used in conjunction… 
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Papers overview

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2010
2010
Neural network modeling for Ni(II) removal from aqueous system using shelled Moringa oleifera seed powder as an agricultural waste.
A single-layer Artificial Neural Network (ANN) model was developed to predict the removal efficiency of Ni(II) ions from aqueous… 
Highly Cited
2009
Highly Cited
2009
Future Generation Information Technology, First International Conference, FGIT 2009, Jeju Island, Korea, December 10-12, 2009. Proceedings
Keynotes.- Computer Science: Where Is the Next Frontier?.- Video Forgery.- Data Analysis, Data Processing, Advanced Computation… 
2008
2008
Automated Textile Defect Recognition System Using Computer Vision and Artificial Neural Networks
Least Development Countries (LDC) like Bangladesh, whose 25% revenue earning is achieved from Textile export, requires producing… 
2008
2008
HYPERSPECTRAL IMAGES FOR UNCERTAINTY INFORMATION INTERPRETATION BASED ON FUZZY CLUSTERING AND NEURAL NETWORK
Effective and understanding exploration of hyperspectral remote sensing data necessitates the development of sophisticated… 
Highly Cited
1998
Highly Cited
1998
Neural Network Studies. 3. Variable Selection in the Cascade-Correlation Learning Architecture
Pruning methods for feed-forward artificial neural networks trained by the cascade-correlation learning algorithm are proposed… 
1997
1997
AN OBJECTIVE-GUIDED ORTHO-SYNAPSE HOPFIELD NETWORK APPROACH TO MACHINE GROUPING PROBLEMS
This paper reports an ortho-synapse Hopfield network (OSHN) for solving machine grouping problems. An objective-guided search… 
1997
1997
Development of Both Linear and Nonlinear Methods To Predict the Liquid Viscosity at 20 C of Organic Compounds
Experimental values for the liquid viscosity (η) at 20 °C ranging from 0.164 mPa·s (trans-2-pentene) to 1490 mPa·s (glycerol… 
1995
1995
Comments on "Noise injection into inputs in back propagation learning"
The generalization capacity of neural networks learning from examples is important. Several authors showed experimentally that… 
Highly Cited
1993
Highly Cited
1993
Prediction of boiling points of organic heterocyclic compounds using regression and neural network techniques
High quality models which relate structural descriptors to normal boiling points have been developed for large, diverse groups of… 
Highly Cited
1988
Highly Cited
1988
Parallel architectures for artificial neural nets
The authors advocate digital VLSI architectures for implementing a wide variety of artificial neural nets (ANNs). A programmable…