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In this paper, the problem of Lyapunov Exponents (LEs) computation from chaotic time series based on Jacobian approach by using polynomial modelling is considered. The embedding dimension which is an important reconstruction parameter, is interpreted as the most suitable order of model. Based on a global polynomial model fitting to the given data, a novel criterion for selecting the suitable embedding...
Being able to predict the coming seizure can impressively improve the quality of the patients' lives since they can be warned to avoid doing risky activities via a prediction system. Here, a locally linear neuro fuzzy model is used to predict the EEG time series. Subsequently, this model is utilized in accompany with Singular Spectrum Analysis for prediction. Afterward, an information theoretic criterion...
Polynomials are one of the most powerful functions that have been used in many fields of mathematics such as curve fitting and regression. Low order polynomials are desired for their smoothness, good local approximation and interpolation. Being smooth, they can be used to locally approximate almost any derivable function. This means that when linear functions fail in approximation (e.g. where the...
In this paper, we propose a new approach to identify a neuro-fuzzy model. In our approach, data space is partitioned indirectly through a fuzzy clustering method. The clusters are not created directly through spatial features of data points. A gradient vector is defined as major feature of clustering in data space. This feature is estimated for each incoming data points. Creating and updating fuzzy...
In this study, new approach based on brain emotional Learning process is presented to predict chaotic system more accurate than other learning models. So the main scope of this paper is to reveal the advantages of this learning model that imitate the internal representation of brain emotional learning model to provide a correct response to stimuli to state a purposeful predicting system. The convergence...
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