Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Fuzzy prediction and filtering in impulsive noise
Fuzzy Sets and Systems - Special issue on fuzzy signal processing
Fuzzy throttle and brake control for platoons of smart cars
Fuzzy Sets and Systems
Fast learning in networks of locally-tuned processing units
Neural Computation
Space-time modeling of traffic flow
Computers & Geosciences
Fuzzy function approximation with ellipsoidal rules
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Modeling gunshot bruises in soft body armor with an adaptive fuzzy system
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Forecasting of traffic flow is one of the most important approaches to control the capacity of highway network efficiently during peak flow periods. Therefore, many emerging methods have been designed to predict traffic flow of freeways. However, the ellipsoidal neural fuzzy model, originally developed for control and pattern recognition problems, was seldom used in forecasting traffic flow. The aim of this study is to investigate the potential of ellipsoidal neural fuzzy model in predicting highway traffic. Monthly traffic data at Tai-Shan tollgate of a freeway in Taiwan are collected to depict the performance of forecasting models. Three other neural network models, namely back-propagation neural networks (BPNN), and radial basis function neural networks (RBFNN) and general regression neural networks (GRNN) models are used to predict the same traffic data sets. Simulation results reveal that the ellipsoidal neural fuzzy time-series (ENFTS) model is superior to the other models. Therefore, the ENFTS is a feasible and promising approach in predicting freeway traffic.