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Recursive Bayesian Recurrent Neural Networks for Time-Series Modeling

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2 Author(s)
Mirikitani, D.T. ; Dept. of Comput., Univ. of London, London, UK ; Nikolaev, N.

This paper develops a probabilistic approach to recursive second-order training of recurrent neural networks (RNNs) for improved time-series modeling. A general recursive Bayesian Levenberg-Marquardt algorithm is derived to sequentially update the weights and the covariance (Hessian) matrix. The main strengths of the approach are a principled handling of the regularization hyperparameters that leads to better generalization, and stable numerical performance. The framework involves the adaptation of a noise hyperparameter and local weight prior hyperparameters, which represent the noise in the data and the uncertainties in the model parameters. Experimental investigations using artificial and real-world data sets show that RNNs equipped with the proposed approach outperform standard real-time recurrent learning and extended Kalman training algorithms for recurrent networks, as well as other contemporary nonlinear neural models, on time-series modeling.

Published in:

Neural Networks, IEEE Transactions on  (Volume:21 ,  Issue: 2 )

Date of Publication:

Feb. 2010

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