Nonlinear time series analysis
Nonlinear time series analysis
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Prediction of Chaotic Time Series Based on Neural Network with Legendre Polynomials
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
An Algorithm of Predictions for Chaotic Time Series Based on Volterra Filter
ISECS '09 Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 02
ARMA lattice identification: a new hereditary algorithm
IEEE Transactions on Signal Processing
IIR Volterra filtering with application to bilinear systems
IEEE Transactions on Signal Processing
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Newborn cry analysis is a difficult task due to its nonstationary nature, combined to the presence of nonlinear behavior as well. Therefore, an adaptive hereditary optimization algorithm is implemented in order to avoid the use of windowing nor overlapping to capture the transient signal behavior. Identification of the linear part of this particular time series is carried out by employing an Autorregresive Moving Average (ARMA) structure; then, the resultant estimation error is approched by a Nonlinear Autorregresive Moving Average (NARMA) model, which realizes a Volterra cubic kernel by means of a bilinear homogeneous structure in order to capture burst behavior. Normal, deaf, asfixia, pain, and uncommon newborn cries are inspected for differentation.