Estimating embedding parameters using structural learning of neural network

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.