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ANSER: Adaptive Neuron Artificial Neural Network System for Estimating rainfall


Zhang, M and Xu, S and Fulcher, JA, ANSER: Adaptive Neuron Artificial Neural Network System for Estimating rainfall, International Journal of Computers & Applications, 29, (3) pp. 215-222. ISSN 1206-212X (2007) [Refereed Article]

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Copyright 2007 ACTA Press

DOI: doi:10.2316/Journal.202.2007.3.202-1585


We propose a new neural network model, Neuron-Adaptive artificial neural Network (NAN). A learning algorithm is derived to tune both the neuron activation function free parameters and the connection weights between neurons. We proceed to prove that a NAN can approximate any piecewise continuous function to any desired accuracy, and then relate the approximation properties of NAN models to some special mathematical functions. A neuron-Adaptive artificial Neural network System for Estimating Rainfall (ANSER), which uses NAN as its basic reasoning network, is described. Empirical results show that the NAN model performs about 1.8% better than artificial neural network groups, and around 16.4% better than classical artificial neural networks when using a rainfall estimate experimental database. The empirical results also show that by using the NAN model, ANSER plus can (1) automatically compute rainfall amounts ten times faster; and (2) reduce average errors of rainfall estimates for the total precipitation event to less than 10%.

Item Details

Item Type:Refereed Article
Keywords:Adaptive neuron, NANN system, estimating, rainfall
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Neural networks
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Xu, S (Dr Shuxiang Xu)
ID Code:65201
Year Published:2007
Deposited By:Information and Communication Technology
Deposited On:2010-10-13
Last Modified:2010-10-14
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