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Chaotic Time Series Prediction Based on a Novel Robust Echo State Network

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3 Author(s)
Decai Li ; Fac. of Electron. Inf. & Electr. Eng., Dalian Univ. of Technol., Dalian, China ; Min Han ; Jun Wang

In this paper, a robust recurrent neural network is presented in a Bayesian framework based on echo state mechanisms. Since the new model is capable of handling outliers in the training data set, it is termed as a robust echo state network (RESN). The RESN inherits the basic idea of ESN learning in a Bayesian framework, but replaces the commonly used Gaussian distribution with a Laplace one, which is more robust to outliers, as the likelihood function of the model output. Moreover, the training of the RESN is facilitated by employing a bound optimization algorithm, based on which, a proper surrogate function is derived and the Laplace likelihood function is approximated by a Gaussian one, while remaining robust to outliers. It leads to an efficient method for estimating model parameters, which can be solved by using a Bayesian evidence procedure in a fully autonomous way. Experimental results show that the proposed method is robust in the presence of outliers and is superior to existing methods.

Published in:

Neural Networks and Learning Systems, IEEE Transactions on  (Volume:23 ,  Issue: 5 )

Date of Publication:

May 2012

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