# Rprop

## Papers overview

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2013

2013

- ESANN
- 2013

Gaussian processes are a powerful tool for non-parametric regression. Training can be realized by maximizing the likelihood of… (More)

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2008

2008

- Chinese Control and Decision Conference
- 2008

RPROP is a fast BP algorithm in which the weights are adjusted local adaptively and the parameters can be chosen easily. The… (More)

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2005

2005

- 2005

Gradient-based optimization algorithms are the standard methods for adapting the weights of neural networks. The natural gradient… (More)

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2005

2005

- The 14th IEEE International Conference on Fuzzy…
- 2005

An adaptive learning method for recurrent fuzzy systems is proposed. The method modifies the SARPROP algorithm, originally… (More)

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2003

Highly Cited

2003

- Neurocomputing
- 2003

The Rprop algorithm proposed by Riedmiller and Braun is one of the best performing -rst-order learning methods for neural… (More)

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2003

2003

- 2003

This paper introduces an efficient modification of the Rprop algorithm for training neural networks. The convergence of the new… (More)

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2000

Highly Cited

2000

- 2000

The Rprop algorithm proposed by Riedmiller and Braun is one of the best performing first-order learning methods for neural… (More)

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1997

1997

- IWANN
- 1997

N.K. Treadgold and T.D. Gedeon School of Computer Science & Engineering The University of New South Wales Sydney N.S.W. 2052… (More)

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1994

Highly Cited

1994

- 1994

A. General Description Rprop stands for 'Resilient backpropagation' and is a local adaptive learning scheme, performing… (More)

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1993

Highly Cited

1993

- 1993

A new learning algorithm for multilayer feedforward networks, RPROP, is proposed. To overcome the inherent disadvantages of pure… (More)

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