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Rprop

Known as: Resilient backpropagation, Resilient propagation 
Rprop, short for resilient backpropagation, is a learning heuristic for supervised learning in feedforward artificial neural networks. This is a… 
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Related topics

Related topics

11 relations
AlgorithmArtificial neural networkBackpropagationDeep learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2017
2017
Image Compression Using Neural Network for Biomedical Applications
As images are of large size and require huge bandwidth and large storage space, an effective compression algorithm is essential… 
2011
2011
Effectiveness of PSO Based Neural Network for Seasonal Time Series Forecasting
Recently, the Particle Swarm Optimization (PSO) technique has gained much attention in the field of time series forecasting… 
2008
2008
Parallelizing neural network training for cluster systems
We present a technique for parallelizing the training of neural networks. Our technique is designed for parallelization on a… 
2008
2008
Estimating landmark locations from geo-referenced photographs
The problem of estimating the positions of landmarks using a mobile robot equipped with a camera has intensively been studied in… 
2006
2006
Hybrid Training of Feed-Forward Neural Networks with Particle Swarm Optimization
Training neural networks is a complex task of great importance in problems of supervised learning. The Particle Swarm… 
2005
2005
Hourly Forecasting of SO2 Pollutant Concentration Using an Elman Neural Network
In this paper the first results produced by an Elman neural network for hourly SO2 ground concentration forecasting are presented… 
1998
1998
Speeding Up Backpropagation Algorithms by Using Cross-Entropy Combined with Pattern Normalization
This paper demonstrates how the backpropagation algorithm (BP) and its variants can be accelerated significantly while the… 
1997
1997
A Cascade Network Algorithm Employing Progressive RPROP
Cascade Correlation (Cascor) has proved to be a powerful method for training neural networks. Cascor, however, has been shown not… 
1997
1997
Computational neural networks for mapping calorimetric data: Application of feed-forward neural networks to kinetic parameters determination and signals filtering
Feedforward neural networks have been used for kinetic parameters determination and signal filtering in differential scanning… 
1997
1997
Adaptation of Learning Rule Parameters Using a Meta Neural Network
This paper proposes an application-independent method of automating learning rule parameter selection using a form of supervisor… 
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