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Indexed by:期刊论文

Date of Publication:2012-05-01

Journal:IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

Included Journals:SCIE、EI、Scopus

Volume:23

Issue:5

Page Number:787-799

ISSN No.:2162-237X

Key Words:Echo state network (ESN); Laplace likelihood function; robust model; surrogate function

Abstract: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.