Neural Computation
A robust shot transition detection method based on support vector machine in compressed domain
Pattern Recognition Letters
A hybrid genetic algorithm and particle swarm optimization for multimodal functions
Applied Soft Computing
Real-Time Freeway Traffic State Estimation Based on Extended Kalman Filter: A Case Study
Transportation Science
Expert Systems with Applications: An International Journal
Traffic Flow Forecasting Algorithm Using Simulated Annealing Genetic BP Network
ICMTMA '10 Proceedings of the 2010 International Conference on Measuring Technology and Mechatronics Automation - Volume 03
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 1
An Approach to Estimating Product Design Time Based on Fuzzy -Support Vector Machine
IEEE Transactions on Neural Networks
An evolutionary algorithm that constructs recurrent neural networks
IEEE Transactions on Neural Networks
Fuzzy artificial neural network p, d, q model for incomplete financial time series forecasting
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In order to improve forecasting accuracy of urban traffic flow, this paper applies support vector regression (SVR) model with Gauss loss function (namely Gauss-SVR) to forecast urban traffic flow. By using the input historical flow data as the validation data, the Gauss-SVR model is dedicated to reduce the random error of the traffic flow data sequence. The chaotic cloud particle swarm optimization algorithm (CCPSO) is then proposed, based on cat chaotic mapping and cloud model, to optimize the hyper parameters of the Gauss-SVR model. Finally, the Gauss-SVR model with CCPSO is established to conduct the urban traffic flow forecasting. Numerical example results have proved that the proposed model has received better forecasting performance compared to existing alternative models. Thus, the proposed model has the feasibility and the availability in urban traffic flow forecasting fields.