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Chaotic time series prediction based on a novel robust echo state network

IEEE transaction on neural networks and learning systems, 2012-05, Vol.23 (5), p.787

EISSN: 2162-2388 ;DOI: 10.1109/tnnls.2012.2188414 ;PMID: 24806127

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  • Title:
    Chaotic time series prediction based on a novel robust echo state network
  • Author: Li, Decai ; Han, Min ; Wang, Jun
  • Subjects: Algorithms ; Computer Simulation ; Models, Statistical ; Neural Networks (Computer) ; Nonlinear Dynamics ; Pattern Recognition, Automated - methods
  • Is Part Of: IEEE transaction on neural networks and learning systems, 2012-05, Vol.23 (5), p.787
  • Description: 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.
  • Publisher: United States
  • Language: English
  • Identifier: EISSN: 2162-2388
    DOI: 10.1109/tnnls.2012.2188414
    PMID: 24806127
  • Source: MEDLINE
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