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'''Almeida–Pineda recurrent backpropagation''' is an extension to the [[Backpropagation|backpropagation]] [[Algorithm|algorithm]] that is applicable to recurrent neural networks. It is a type of [[Supervised learning|supervised learning]]. It was described somewhat cryptically in Richard Feynman's senior thesis, and rediscovered independently in the context of artificial neural networks by both Fernando Pineda and Luis B. Almeida.<ref name=Feynman1939>{{cite journal|last=Feynman|first=Richard P.|title=Forces in Molecules|journal=Physical Review|publisher=American Physical Society|date=August 1939|volume=56|issue=4|pages=340–3|doi=10.1103/PhysRev.56.340|bibcode=1939PhRv...56..340F}}</ref><ref name=Pineda1989>{{cite journal|last=Pineda|first=Fernando|title="Generalization of Back-Propagation to Recurrent Neural Networks|journal=Physical Review Letters|volume=19|issue=59|date=9 November 1987|pages=2229–32|doi=10.1103/PhysRevLett.59.2229|pmid=10035458|bibcode=1987PhRvL..59.2229P}}</ref><ref name=Almeida1987>{{cite conference|last=Almeida|first=Luis B.|title=A learning rule for asynchronous perceptrons with feedback in a combinatorial environment|date=June 1987|conference=IEEE First International Conference on Neural Networks|location=San Diego, CA, USA|pages=608–18}}</ref>
A recurrent neural network for this algorithm consists of some input units, some output units and eventually some hidden units.

For a given set of (input, target) states, the network is trained to settle into a stable activation state with the output units in the target state, based on a given input state clamped on the input units.

==References==
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[[Category:Machine learning algorithms]]
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