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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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AI winterALOPEXAdaBoostAutoencoder

Papers overview

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2012
2012
Modelling and predicting electricity consumption using artificial neural networks
Electricity has overtime become one of the most important forms of energy to man. One of the key concerns of the electricity… 
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… 
2004
2004
Exploring QSAR of Non-Nucleoside Reverse Transcriptase Inhibitors by Neural Networks: TIBO Derivatives
Abstract: Human Immunodeficiency Virus type 1 (HIV-1) reverse transcriptase is an important target for chemotherapeutic agents… 
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… 
1997
1997
Figure 4. Typical Evolution of a Population of Networks Generated by (a) Mta Algorithm, (b) Cca Algorithm and (c) Ccas Algorithm. (a) (b) 6. Acknowledgments 7. References
[4] F. Dellaert, and J. Vandewalle, " Automatic design of cellular neural networks by means of genetic algorithms: finding a… 
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…