Paul Werbos

Paul J. Werbos (born 1947) is a scientist best known for his 1974 Harvard University Ph.D. thesis, which first described the process of training… (More)
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Topic mentions per year

Topic mentions per year

1988-2017
012319882017

Papers overview

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2007
2007
A number of success stories have been told where reinforcement learning has been applied to problems in continuous state spaces… (More)
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2007
2007
The feed-forward multilayer networks (perceptrons, radial basis function networks (RBF), probabilistic networks, etc.) are… (More)
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2001
2001
Biological organisms display an amazing ability during their ontogenetic development to adaptively develop solutions to the… (More)
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2000
2000
The generalized maze problem has been considered as an interesting testbed by various researchers in AI and neural networks. The… (More)
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Highly Cited
1992
Highly Cited
1992
goals into their corresponding sensory realization, without regard to the actions needed to achieve the goals. Envisioning… (More)
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1991
1991
  • J Urgen Schmidhuber
  • 1991
Much of the recent research on adaptive neuro-control and reinforcement learning focusses on systems with adaptivèworld models… (More)
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1991
1991
The two well known learning algorithms of recurrent neural networks are the back-propagation (Rumelhart & el al., Werbos) and the… (More)
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1990
1990
An on-line learning algorithm for reinforcement learning with continually running recurrent networks in non-stationary reactive… (More)
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Highly Cited
1989
Highly Cited
1989
The forward modeling approach is a methodology for learning control when data is available in distal coordinate systems. We… (More)
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Review
1988
Review
1988
-Backpropagation is often viewed as a method for adapting artificial neural networks to classify patterns. Based on parts of the… (More)
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