Multilayer feedforward networks are universal approximators
Neural Networks
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
Neural networks for localized approximation
Mathematics of Computation
A Theory of Networks for Approximation and Learning
A Theory of Networks for Approximation and Learning
A general regression neural network
IEEE Transactions on Neural Networks
Existence and uniqueness results for neural network approximations
IEEE Transactions on Neural Networks
Advances in Engineering Software
Motion control with deadzone estimation and compensation using GRNN for TWUSM drive system
Expert Systems with Applications: An International Journal
Neuro semantic thresholding using OCR software for high precision OCR applications
Image and Vision Computing
Desirability improvement of committee machine to solve multiple response optimization problems
Advances in Artificial Neural Systems
| Hi-index | 0.98 |
Application of soft computational methods, especially artificial neural networks, in examining individual traveller behaviour is not encountered frequently. In most of the relevant cited papers, the feed-forward back propagation neural network (FFBPNN) models or hybrid models of FFBPNNs are proposed. However the feed-forward back propagation algorithm has some drawbacks, which can easily lead the model to develop in an inaccurate direction. Throughout this study, two different algorithms, radial basis function neural network (RBFNN) and generalized regression neural network (GRNN), are employed to propose a new calibration process for travel mode choice analysis in a transportation modelling framework. The neural network methods are not applied directly to calibrate models but are used as a sub-process for alternative non-linear model specification on utility function. Results show both the surpassing of RBFNNs and GRNNs over frequently used FFBPNNs, and the superiority of neural network methods over a conventional statistical model, multivariate linear regression, during mode choice calibrations. Also having experienced the existence of a claim that ANNs can tackle the problem of travel choice modelling as well as, if not better than, the discrete choice approach [D.A. Hensher, T.T. Ton, A comparison of the predictive potential of artificial neural networks and nested logit models for commuter mode choice, Transp. Res., Part E Logist. Trans. Rev. 36 (3) (2000) 155-172], use of such soft computing tools in studying traveller behaviour should be an autonomous part of a calibration process.