Title :
Predicting and modelling of nonstationary temporal signals with fractal characteristics
Author :
Mo, F. ; Kinsner, W.
Author_Institution :
Dept. of Electr. & Comput. Eng., Manitoba Univ., Winnipeg, Man., Canada
Abstract :
This paper presents a scheme for predicting and modelling of nonstationary signals possessing fractal characteristics, using a resource-allocating network (RAN). One significant feature of a RAN is its ability to allocate resources corresponding to the complexity of nonstationary signals, thus tracking and matching the complexity of nonstationary signals can be achieved. The experimental results of predicting chaotic time series and short-term power load have shown RAN is suitable for modelling and predicting such nonstationary signals with the fundamental advantage of complexity matching and tracking capability
Keywords :
chaos; fractals; load forecasting; neural nets; power system analysis computing; signal processing; time series; chaotic time series prediction; fractal characteristics; nonstationary signals modelling; nonstationary signals prediction; nonstationary temporal signals; resource-allocating network; short-term power load prediction; 1f noise; Abstracts; Chaos; Computer architecture; Differential equations; Fractals; Neural networks; Predictive models; Radio access networks; Resource management;
Conference_Titel :
Electrical and Computer Engineering, 1998. IEEE Canadian Conference on
Conference_Location :
Waterloo, Ont.
Print_ISBN :
0-7803-4314-X
DOI :
10.1109/CCECE.1998.685563
