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Genetic algorithm (GA) is used for jointly estimating the embedding dimension and time lag parameters in order to achieve an optimal reconstruction of time series in state space. The conventional methods (false nearest neighbours and first minimum of the mutual information for estimating the embedding dimension and time lag, respectively) are also included for comparison purposes. The performance...
This paper proposes the use of exponential autoregressive (EAR) model for modelling of time series that are known to exhibit non-linear dynamics such as random fluctuations of amplitude and frequency. Biological signal (bio-signal) such as electroencephalogram (EEG) is known to exhibit nonlinear dynamics. Such signals cannot be modelled with traditional linear modelling techniques like autoregressive...
In this paper, we explore the use of genetic algorithm (GA) to select a minimum number of channels that identifies individuals based on brain signals i.e. electroencephalogram (EEG). The fusion of GA with linear discriminant classifier shows that the identification performance of EEG signals from 40 subjects does not degrade when using 23 selected channels as compared to all the available 61 channels...
A genetic algorithm is proposed for ordering the input patterns during training for simplified fuzzy ARTMAP (SFA) classifier to improve the individual identification classification performance using brain fingerprints. The results indicate improved classification performance as compared to the existing methods for pattern ordering, namely voting strategy and min-max. As the ordering method is general,...
A technique is proposed to reduce additive noise from biomedical signals that have high kurtosis values using genetic algorithm (GA). The technique is applied to reduce multiple linear additive noises from electrocardiogram (ECG) signals, which have high kurtosis values due to the presence of R peaks. This GA method uses the basic principles of independent component analysis (ICA) and could also be...
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