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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...
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...
The cyclic solar activity has significant effects on earth, climate, satellites and space missions. Several methods have been introduced for the prediction of sunspot number, which is a common measure of solar activity. In this study a co-evolutionary algorithm is presented for inferring the topology and parameters of a multilayered neural network with the minimum of experimentation to the sunspot...
To simply visualize and analyze the behavior of economic time series, a common tool is the Japanese candlestick chart which is widely used in modern technical analysis methods. What traders refer to as the candlestick patterns implicitly reveals the existence of some systematic dynamics behind most of economic time series, which can also be unveiled by modern nonlinear models and tests. This paper...
In this paper we investigate the dynamic modeling of chaotic systems by using neural networks. It is possible for a neural network to approximate a continous fuction f(x1, …, Xn), enabling us to construct a static model for chaotic system with precision e > 0. It is shown that the dynamic model of a chaotic system can also be costructed with a precision e > 0 as well as a limited prediction...
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