A nature inspired Ying-Yang approach for intelligent decision support in bank solvency analysis
Expert Systems with Applications: An International Journal
An online Bayesian Ying-Yang learning applied to fuzzy CMAC
Neurocomputing
Travel Speed Prediction Using Fuzzy Reasoning
ICIRA '08 Proceedings of the First International Conference on Intelligent Robotics and Applications: Part I
Study to short-term flow estimation at intersection base on genetic neural networks
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Intelligent Traffic Control Decision Support System
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
A novel self-organizing fuzzy rule-based system for modelling traffic flow behaviour
Expert Systems with Applications: An International Journal
A novel brain-inspired neural cognitive approach to SARS thermal image analysis
Expert Systems with Applications: An International Journal
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 6
A novel application of a neuro-fuzzy computational technique in event-based rainfall-runoff modeling
Expert Systems with Applications: An International Journal
Decision support for coordinated road traffic control actions
Decision Support Systems
| Hi-index | 0.01 |
Although much research has been done over the decades on the formulation of statistical regression models for road traffic relationships, they have been largely unsuitable due to the complexity of traffic characteristics. Traffic engineers have resorted to alternative methods such as neural networks, but despite some promising results, the difficulties in their design and implementation remain unresolved. In addition, the opaqueness of trained networks prevents understanding the underlying models. Fuzzy neural networks, which combine the complementary capabilities of both neural networks and fuzzy logic, thus constitute a more promising technique for modeling traffic flow. This paper describes the application of a specific class of fuzzy neural network known as the pseudo outer-product fuzzy neural network using the truth-value-restriction method (POPFNN-TVR) for short-term traffic flow prediction. The obtained results highlight the capability of POPFNN-TVR in fuzzy knowledge extraction and generalization from input data as well its high degree of prediction capability as compared to traditional feedforward neural networks using backpropagation learning.