In this paper, an improvement to the E step of the EM algorithm for nonlinear state-space models is presented. We also propose strategies for model structure selection when the EM-...
Frequently, sequences of state transitions are triggered by specific signals. Learning these triggered sequences with recurrent neural networks implies storing them as different at...
In this paper we firstly analysis the chaotic characters of three sets of the financial time series (Hang Sheng Index (HIS), Shanghai Stock Index and US gold price) based on the ph...
Recurrent Self-Organizing Map (RSOM) is studied in three di erent time series prediction cases. RSOM is used to cluster the series into local data sets, for which corresponding lo...
Timo Koskela, Markus Varsta, Jukka Heikkonen, Kimm...
One of the key problems in forming a smooth model from input-output data is the determination of which input variables are relevant in predicting a given output. In this paper we ...
Alban P. M. Tsui, Antonia J. Jones, A. Guedes de O...