Please use this identifier to cite or link to this item:
|Title:||Learning sunspot series dynamics by recurrent neural networks|
|Source:||In WK Ching & MKP Ng (Eds.), Advances in data mining and modeling, p. 107-115. Singapore ; River Edge, NJ: World Scientific, 2003 How to cite?|
|Abstract:||Sunspot series is a record of the activities of the surface of the sun. It is chaotic and is a well-known challenging task for time series analysis. In this paper, we show that we can approximate the transformed sequence with a discrete-time recurrent neural networks. We apply a new smoothing technique by integrating the original sequence twice with mean correction and also normalize the smoothened sequence to [-1,1]. The smoothened sequence is divided into a few segments and each segment is approximated by a neuron of a discrete-time fully connected neural network. Our approach is based on the universal approximation property of discrete-time recurrent neural network. The relation between the least square error and the network size are discussed. The results are compared with the linear time series models.|
|Appears in Collections:||Book Chapter|
Show full item record
Checked on Feb 19, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.