DocumentCode :
1802983
Title :
Multiplicative-additive neural networks with active neurons
Author :
Valenca, Meuser ; Ludermir, Teresa
Author_Institution :
Companhia Hidro-Eletrica, Sao Francisco
Volume :
6
fYear :
1999
fDate :
36342
Firstpage :
3821
Abstract :
An artificial neural network is a flexible mathematical structure which is capable of identifying complex nonlinear relationships between input and output data sets. Such neural networks have been characterized by passive neurons that are not able to select and estimate their own inputs. In a new approach, which corresponds in a better way to the actions of human nervous system, the connections between several neurons are not fixed but change in dependence on the neurons themselves. This paper deals with the applications of the self-organization multiplicative-additive algorithm with active neurons to prediction models of river flow. The nonlinear multiplicative-additive model approach is shown to provide better representation of the weekend average water inflow forecasting in comparison to the models based on the Box-Jenkins method, currently in use on the Brazilian Electrical Sector
Keywords :
forecasting theory; natural resources; rivers; self-organising feature maps; active neurons; forecasting theory; multiplicative-additive neural networks; prediction models; river flow; self-organization; Artificial neural networks; Autoregressive processes; Biological neural networks; Calibration; Mathematical model; Neural networks; Neurons; Predictive models; Rivers; Water resources;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
ISSN :
1098-7576
Print_ISBN :
0-7803-5529-6
Type :
conf
DOI :
10.1109/IJCNN.1999.830763
Filename :
830763
Link To Document :
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