Application of radial basis function and generalized regression neural networks in nonlinear utility function specification for travel mode choice modelling

DOI10.1016/J.MCM.2006.02.002zbMath1138.62342OpenAlexW2037633404MaRDI QIDQ2476117

Hilmi Berk Celikoglu

Publication date: 11 March 2008

Published in: Mathematical and Computer Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.mcm.2006.02.002

zbMATH Keywords

artificial neural networkradial basis function neural networkgeneralized regression neural networkfeed-forward back propagation neural networktransportation mode choice

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Learning and adaptive systems in artificial intelligence (68T05) Transportation, logistics and supply chain management (90B06) Applications of statistics (62P99) Neural nets and related approaches to inference from stochastic processes (62M45)

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Uses Software

• KernSmooth

Cites Work

• Multilayer feedforward networks are universal approximators
• Limitations of the approximation capabilities of neural networks with one hidden layer
• Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks
• Neural Networks for Localized Approximation
• On Non-Parametric Estimates of Density Functions and Regression Curves
• Learning representations by back-propagating errors
• Approximation by superpositions of a sigmoidal function
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