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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
Notas:
ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ObjectType-Article-1
ObjectType-Feature-2
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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