Title: Weather Forecasting Using
Different
Neural Networks Classes
Abstract: Accurate
weather predictions are important for planning our day-to-day
activities.
In recent years, a large literature has evolved on the use of
artificial
neural networks (NNs) in many forecasting applications.Neural networks
are
particularly appealing because of their ability to model an unspecified
non-linear
relationship between weather variables. This paper evaluates two neural
networks
architectures in this context: the popular multilayer perceptron (MLP),
and
the radial basis function network (RBF). Comparisons are also made
between
those neural networks architectures at different training and testing
scenarios.
Simulation results for each scenario is demonstrate the effectives of
both
neural network architecture.
Author: Hatem
Mohamed
Abdul-Kader