Predicting Chaotic System Behavior using Machine Learning Techniques

The RCs have been developed from the original Echostate Time series data have attracted significant attention across network (ESN)-based to nonlinear vector autoregression various fields in the natural and social sciences because of (NVAR), which also called NG-RC [18]. An NVAR machine is their potential applications. The analysis and prediction of time created where the feature vector is composed of time-delayed series data have been the focus of extensive research over the observations of the dynamical system, along with nonlinear past few decades [1]-[5]. Chaotic time series are among the functions of these observations. It requires no random matrices, most complex because even small perturbation in initial values fewer metaparameters, and provides interpretable results can lead to significant variations in their behaviors. Due to which reflects the nature of the nonlinear model. In addition, their sensitivity to initial conditions, it is a challenging task to it is 33 162 times less costly to simulate than a typical predict chaotic time behaviors.