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

Li, Decai ; Han, Min ; Wang, Jun

IEEE transaction on neural networks and learning systems, 2012-05, Vol.23 (5), p.787-799

New York, NY: IEEE

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Título:
Chaotic Time Series Prediction Based on a Novel Robust Echo State Network
Autor: Li, Decai ; Han, Min ; Wang, Jun
Assuntos: Algorithms ; Applied sciences ; Artificial intelligence ; Bayesian analysis ; Bayesian methods ; Computer science; control theory; systems ; Computer Simulation ; Connectionism. Neural networks ; Echo state network (ESN) ; Estimating ; Exact sciences and technology ; Laplace likelihood function ; Learning ; Learning and adaptive systems ; Mathematical model ; Mathematical models ; Models, Statistical ; Networks ; Neural networks ; Neural Networks (Computer) ; Nonlinear Dynamics ; Optimization ; Pattern Recognition, Automated - methods ; Reservoirs ; robust model ; Robustness ; Studies ; surrogate function ; Training ; Training data
É parte de: IEEE transaction on neural networks and learning systems, 2012-05, Vol.23 (5), p.787-799
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Descrição: 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.
Editor: New York, NY: IEEE
Idioma: Inglês

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

Li, Decai ; Han, Min ; Wang, Jun

New York, NY: IEEE

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