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We propose a method for source separation of convolutive mixture based on nonlinear prediction-error filters.
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We tested reconstructions using both Haar and Daubechies wavelets as well as a reconstruction based on nonlinear prediction (see, e.g. [ 48] section 4.2).
Our design scheme is based on nonlinear observers, state elimination, and the inversion of the error equation.
This is important because one cannot include separate regressors based on predictions and prediction errors in the same design matrix.
Neural networks are new methodological tools based on nonlinear models.
Symptoms characterising the current state of the process are obtained based on the prediction errors.
In [9], the prediction filter coefficients are optimized with a least mean squares (LMS) type algorithm based on the prediction error.
In this work, parameter estimation technique based on a prediction error minimization (PEM) framework has been used.
A new criterion based on the prediction errors of fatigue constants is proposed to overcome the limitation.
On the other hand, based on the prediction error criterion, the SCAD showed minimum value indicating better prediction.
For any new prediction, each inductive predictor will give an endpoint prediction (single-value prediction) and produce a prediction interval based on the predicted error, the desired confidence and the list of (alpha) values.
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