The space complexity of approximating the frequency moments
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Regularization in the selection of radial basis function centers
Neural Computation
Fast estimation of fractal dimension and correlation integral on stream data
Information Processing Letters
Nonlinear dynamical factor analysis for state change detection
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
Automatic change detection is the important subject in dynamical systems. There are known techniques for linear and some techniques for nonlinear systems, but merely few of them concern deterministic chaos. This paper presents automatic change detection technique for dynamical systems with chaos based on three different approaches neural network model, fractional dimension and recurrence plot. Control charts are used as a tool for automatic change detection. We consider the dynamical system described by the univariate time series. We assume that change parameters are unknown and the change could be either slight or drastic. Methods are checked by using small data set and stream data.