Parameter estimation using chaotic time series

Parameter Estimation of Loranz Chaotic Dynamic System Using Bees Algorithm

An important problem in nonlinear science is the unknown parameters estimation in Loranz chaotic system. Clearly, the parameter estimation for chaotic systems is a multidimensional continuous optimization problem, where the optimization goal is to minimize mean squared errors (MSEs) between real and estimated responses for a number of given samples. The Bees algorithm (BA) is a new member of me...

Model Based Method for Determining the Minimum Embedding Dimension from Solar Activity Chaotic Time Series

Predicting future behavior of chaotic time series system is a challenging area in the literature of nonlinear systems. The prediction's accuracy of chaotic time series is extremely dependent on the model and the learning algorithm. On the other hand the cyclic solar activity as one of the natural chaotic systems has significant effects on earth, climate, satellites and space missions. Several m...

Оценка параметров хаотического процесса с помощью UKF-фильтра для построения прогноза (Parameter Estimation of Chaotic Process Using UKF and Time Series Forecasting)

A New Cost Function for Parameter Estimation of Chaotic Systems Using Return Maps as Fingerprints

Estimating parameters of a model system using observed chaotic scalar time series data is a topic of active interest. To estimate these parameters requires a suitable similarity indicator between the observed and model systems. Many works have considered a similarity measure in the time domain, which has limitations because of sensitive dependence on initial conditions. On the other hand, there...

Parameter Estimation of a Known Chaotic Time Series Corrupted by White Gaussian Noise

The subject of parameter estimation in linear signals embedded in white Gaussian noise has been extensively studied. The subject of nonlinear choatic signals, however, has received much less attention. This paper will examine some of the known techniques for estimating the parameters of chaotic signals such as iterative maps, including the tent map and the logistic map.