Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Universal approximation using radial-basis-function networks
Neural Computation
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
A Performance evaluation of neural network models in traffic volume forecasting
Mathematical and Computer Modelling: An International Journal
A neuro-computational intelligence analysis of the global consumer software piracy rates
Expert Systems with Applications: An International Journal
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Artificial neural networks (ANNs) are one of the recently explored advanced technologies, which show promise in the area of transportation engineering. The presented study used two different ANN algorithms, feed forward back-propagation (FFBP) and radial basis function (RBF), for the purpose of daily trip flow forecasting. The ANN predictions were quite close to the observations as reflected in the selected performance criteria. The selected stochastic model performance was quite poor compared with ANN results. It was seen that the RBF neural network did not provide negative forecasts in contrast to FFBP applications. Besides, the local minima problem faced by some FFBP algorithms was not encountered in RBF networks.