Title :
Estimating embedding parameters using structural learning of neural network
Author :
Manabe, Yusuke ; Chakraborty, Bishwajit
Author_Institution :
Iwate Prefectural Univ., Japan
Abstract :
Summary form only given. The paper presents a novel approach to estimating the embedding parameters for the reconstruction of the underlying dynamic system from an observed nonlinear time series. The estimation is performed by a feedforward neural network with variance suppressive learning, a kind of structural learning proposed by the authors earlier. It has been found that the proposed method is more efficient than conventional methods for estimating the embedding parameters for reconstruction of the attractor in the phase space. The efficiency of the proposed method has also been verified for short term prediction of a nonlinear time series. The simulation results show that the neural network predictor with selection of parameters from the knowledge of embedding parameters from the proposed scheme is more stable and needs faster training than the neural network predictor with parameters from conventional methods.
Keywords :
feedforward neural nets; learning (artificial intelligence); parameter estimation; phase space methods; prediction theory; signal reconstruction; time series; attractor; embedding parameter estimation; feedforward neural network; neural network predictor; phase space; signal reconstruction; structural learning; time series; variance suppressive learning; Analytical models; Computational efficiency; Feedforward neural networks; Neural networks; Nonlinear systems; Parameter estimation; Performance analysis; Phase estimation; Predictive models; Time series analysis;
Conference_Titel :
Nonlinear Signal and Image Processing, 2005. NSIP 2005. Abstracts. IEEE-Eurasip
Conference_Location :
Sapporo
Print_ISBN :
0-7803-9064-4
DOI :
10.1109/NSIP.2005.1502288