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- W181838265 abstract "We propose a new neural network model – Neuron-Adaptive artificial neural Network (NAN) – is developed. 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, 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% 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 (i) automatically compute rainfall amounts ten times faster; and (ii) reduce average errors of rainfall estimates for the total precipitation event to less than 10 per cent." @default.
- W181838265 created "2016-06-24" @default.
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- W181838265 date "2007-01-01" @default.
- W181838265 modified "2023-09-27" @default.
- W181838265 title "ANSER: an Adaptive-Neuron Artificial Neural Network System for Estimating Rainfall Using Satellite Data" @default.
- W181838265 hasPublicationYear "2007" @default.
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