A Multiple Model Approach to Time-Series Prediction Using an Online Sequential Learning Algorithm

2019 ◽  
Vol 49 (5) ◽  
pp. 976-990 ◽  
Author(s):  
Koshy George ◽  
Prabhanjan Mutalik
1997 ◽  
Vol 9 (2) ◽  
pp. 461-478 ◽  
Author(s):  
Lu Yingwei ◽  
N. Sundararajan ◽  
P. Saratchandran

This article presents a sequential learning algorithm for function approximation and time-series prediction using a minimal radial basis function neural network (RBFNN). The algorithm combines the growth criterion of the resource-allocating network (RAN) of Platt (1991) with a pruning strategy based on the relative contribution of each hidden unit to the overall network output. The resulting network leads toward a minimal topology for the RBFNN. The performance of the algorithm is compared with RAN and the enhanced RAN algorithm of Kadirkamanathan and Niranjan (1993) for the following benchmark problems: (1) hearta from the benchmark problems database PROBEN1, (2) Hermite polynomial, and (3) Mackey-Glass chaotic time series. For these problems, the proposed algorithm is shown to realize RBFNNs with far fewer hidden neurons with better or same accuracy.


2007 ◽  
Vol 90 (12) ◽  
pp. 129-139
Author(s):  
Manabu Gouko ◽  
Yoshihiro Sugaya ◽  
Hirotomo Aso

2006 ◽  
Vol 17 (6) ◽  
pp. 1411-1423 ◽  
Author(s):  
Nan-Ying Liang ◽  
Guang-Bin Huang ◽  
P. Saratchandran ◽  
N. Sundararajan

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