M. Zhang, S. Xu, and J. Fulcher
Adaptive neuron, NANN system, estimating, rainfall
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%.
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