Three chaotic indicators, namely the correlation dimension, the Lyapunov exponent, and the Kolmogorov entropy, are estimated for one-year long hourly average NO (nitrogen monoxide), CO (carbon monoxide), SO2 (sulfur dioxide), PM10 (particles with an aerodynamic diameter of approximately 10 m or less), and NO2 (nitrogen dioxide) concentration to examine the possible chaotic characteristics in th...

paper presents the advances of a research using a combination of recurrent and feed-forward neural networks for long term prediction of chaotic time series. It is known that point-to-point, long term prediction for chaotic time series is not possible; however, in this research we are looking for ways to build dynamical systems using artificial neural network, that contain the same characteristi...

With the analysis of the technology of phase space reconstruction, a modeling and forecasting technique based on the Radial Basis Function (RBF) neural network for chaotic time series is presented in this paper. The predictive model of chaotic time series is established with the adaptive RBF neural networks and the steps of the chaotic learning algorithm with adaptive RBF neural networks are ex...

This paper employs a general non-linear analysis tool to analyse the nature of time series associated with the price (returns) of a particular company in Tehran Stock Exchange. It is shown that the behavior of the process associated with the price (returns) time-series of this company is weakly chaotic, and due to the non-random behavior of the process, short term prediction of stock price is p...

The aim of this paper is to compare different prediction methods for chaotic deterministic systems. We consider three different methods to evaluate the dynamics of the systems : the Nearest Neighbors, the Radial Basis Functions and the Regression Tree. We use a comparison criterion suited to chaotic systems : the prediction horizon. The optimal prediction horizon is discussed with respect to th...

In this paper, a new method is presented for encryption of colored images. This method is based on using stack data structure and chaos which make the image encryption algorithm more efficient and robust. In the proposed algorithm, a series of data whose range is between 0 and 3 is generated using chaotic logistic system. Then, the original image is divided into four subimages, and these four i...

The nonlinear and dynamic accommodating capability of time domain models makes them a useful representation of chaotic time series for analysis, modeling and prediction. This paper is devoted to the modeling and prediction of chaotic time series with hidden attractors using a nonlinear autoregressive model with exogenous inputs (NARX) based on a novel recurrent fuzzy functions (RFFs) approach. ...

We propose a method that is able to analyze chaotic time series, gained from experimental data. The method allows to identify scalar time-delay systems. If the dynamics of the system under investigation is governed by a scalar time-delay differential equation of the form dy(t) dt = h(y(t), y(t−τ0)), the delay time τ0 and the function h can be recovered. There are no restrictions to the dimensio...

We address under what conditions dynamical coupling between chaotic systems can be detected reliably from scalar time series. In particular, we study weakly coupled chaotic systems and focus on the detectability of the correlation dimension of the chaotic invariant set by utilizing the Grassberger-Procaccia algorithm. An algebraic scaling law is obtained, which relates the necessary length of t...