chaotic systems are nonlinear dynamic systems, the main feature of which is high sensitivity to initial conditions. to initiate a design process in fuzzy model, chaotic systems must first be represented by t-s fuzzy models. in this paper, a new fuzzy modeling method based on sector nonlinearity approach has been recommended for chaotic systems relating to initial condition variations using the ...

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. ...

the speech is an easily accessible signal which clearly represents the characteristics of larynx and vocal folds. therefore, application of some proper machine learning algorithms on a small part of a recorded speech signal may help in non-invasive diagnosing of vocal fold diseases. since there are some experimental evidences that suggest the existence of chaotic behavior in speech production s...

this paper presents a fuzzy approach to the prediction of highly nonlinear timeseries.the optimized mamdani-type fuzzy system denoted sqp-flc is applied forthe input-output modeling of measured data. in order to tune fuzzy membershipfunctions, a sequential quadratic programming (sqp) method is employed. theproposed method is evaluated and validated on a highly complex time series, dailygold pri...

Although many mathematicians have searched on the fractional calculus since many years ago, but its application in engineering, especially in modeling and control, does not have many antecedents. Since there are much freedom in choosing the order of differentiator and integrator in fractional calculus, it is possible to model the physical systems accurately. This paper deals with time-domain id...

This paper presents a new algorithm that combines perturbation theory and genetic programming for modeling and forecasting real-world chaotic time series. Both perturbation theory and time series modeling have to build symbolic models for very complex system dynamics. Perturbation theory does not work without well-defined system equation. Difficulties in modeling time series lie in the fact tha...

Chaotic dynamics may impose severe limits to deterministic modeling by dynamical equations of natural systems. We give theoretical argument that severe modeling difficulties may occur for highdimensional chaotic systems in the sense that no model is able to produce reasonably long solutions that are realized by nature. We make these ideas concrete by investigating systems of coupled chaotic osc...

This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) algorithm is used for Nonlinear Parameter Estimation (NPE) of dynamic model structures. In addition, GPM integrates the results of Nonlinear Time Series Analysis (NTSA) to adjust t...

On the basis of nonlinear dynamical modeling we investigate a chaos-based detector, which allows the extraction of signal frequencies in noisy chaotic interference.The detection scheme is tested by using both computer-generated chaotic data and real-life Lorenz-Sten£o (LS) chaotic circuit data respectively. The performance analysis demonstrates that signals hidden beneath the chaotic ambient no...

A natural process can be deterministically modeled if solutions from its mathematical model stay close to the ones produced by nature. The mathematical model, however, is not exact due to imperfections of the natural system. We describe, in this paper, that there exists a class of models of chaotic processes, for which severe obstruction to deterministic modeling may arise. In particular, such ...