Application of neural network for air-fuel ratio identification in spark ignition engine
International Journal of Computer Applications in Technology
Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions
Expert Systems with Applications: An International Journal
International Journal of Computer Applications in Technology
A bayesian network approach to traffic flow forecasting
IEEE Transactions on Intelligent Transportation Systems
Dynamical optimal training for interval type-2 fuzzy neural network (T2FNN)
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Computing derivatives in interval type-2 fuzzy logic systems
IEEE Transactions on Fuzzy Systems
International Journal of Computer Applications in Technology
Adaptive backstepping sliding model control of hypersonic vehicle based on CMAC and dynamic surface
International Journal of Computer Applications in Technology
International Journal of Computer Applications in Technology
Selection of shielding gas by adaptive AHP decision model
International Journal of Computer Applications in Technology
International Journal of Computer Applications in Technology
Radial basis function network using intuitionistic fuzzy C means for software cost estimation
International Journal of Computer Applications in Technology
Mathematical methods to quantify and characterise the primary elements of trophic systems
International Journal of Computer Applications in Technology
Traffic meteorological visibility estimation based on homogenous area extraction
International Journal of Computer Applications in Technology
Study on urban three-lane mixed traffic flow with buses based on the Nagel-Schreckenberg model
International Journal of Wireless and Mobile Computing
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This paper presents a new prediction model based on interval type-2 fuzzy neural network (IT2FNN) and self-organising learning algorithm. Unlike traditional intelligent prediction models, whose structure and parameters must be predetermined by expert experience or professional knowledge, the IT2FNN model determines its own form by the self-organising structure identification and parameter optimisation algorithm. In the structure identification stage, the hierarchical clustering algorithm which includes lower-layer subtractive clustering and upper-layer FCM clustering is employed to determine the size of the IT2FNN predictor. Then, in the parameters optimisation stage, the steepest gradient descent algorithm is also utilised to optimise the free parameters. Finally, two groups normalised traffic flow data, which came from the 3rd ring freeway, Beijing and I880 urban freeway, California are employed to train and evaluate the IT2FNN predictor. Experiment results have illustrated its effectiveness.