This paper presents a new approach to learning in recurrent neural networks, based on the descent of the error functional in the space of the linear outputs of the neurons (neuron space approach). At each step of the learning process a linear system is solved for the weights using a Recursive Least Squares technique. This approach, with respect to traditional gradient-based algorithms, guarantees better performances from the point of view of both the speed of convergence and the numerical robustness.
Recursive least squares approach to learning in recurrent neural networks / Parisi, Raffaele; DI CLAUDIO, Elio; Rapagnetta, A.; Orlandi, Gianni. - STAMPA. - 2:(1996), pp. 1350-1354. (Intervento presentato al convegno 1996 IEEE International Conference on Neural Networks, ICNN tenutosi a Washington DC, USA nel June 3-6, 1996) [10.1109/ICNN.1996.549095].
Recursive least squares approach to learning in recurrent neural networks
PARISI, Raffaele;DI CLAUDIO, Elio;ORLANDI, Gianni
1996
Abstract
This paper presents a new approach to learning in recurrent neural networks, based on the descent of the error functional in the space of the linear outputs of the neurons (neuron space approach). At each step of the learning process a linear system is solved for the weights using a Recursive Least Squares technique. This approach, with respect to traditional gradient-based algorithms, guarantees better performances from the point of view of both the speed of convergence and the numerical robustness.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
