Almeida–Pineda recurrent backpropagation is an extension to the backpropagation algorithm that is applicable to recurrent neural networks. It is a type of supervised learning.  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. 
Almeida–Pineda recurrent backpropagation is an extension to the backpropagation algorithm that is applicable to recurrent neural networks. It is a type of supervised learning. It was described somewhat cryptically in Richard Feynman's senior thesis, and rediscovered in the context of artificial neural networks by Fernando Pineda and Luis B. Almeida, independently. 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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It was described somewhat cryptically in Richard Feynman's senior thesis, and rediscovered in the context of artificial neural networks by Fernando Pineda and Luis B. Almeida, independently.
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