A global search procedure for parameter estimation in neural spatial interaction modelling

Fischer, Manfréd M.; Hlaváčková‐Schindler, Kateřina; Reismann, Martin

doi:10.1007/s101100050017

Cited by 19 publications

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“…The design of a neural network approach suited to deal with the doubly constrained case is still missing. Finding good hybrid optimization procedures for solving the non-convex learning problems is another important issue for further research even though some relevant work can be found in Fischer, Hlavackova-Schindler and Reismann (1999), Fischer and Reismann (2002a, b).…”

Section: Discussionmentioning

confidence: 99%

Principles of Neural Spatial Interaction Modeling

Fischer

2009

Advances in Spatial Science

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may Principles of Neural SpatialInteraction Modelling MANFRED M. FISCHER Department of Economic Geography & Geoinformatics Vienna University of Economics and Business Administration AbstractNeural spatial interaction models represent the most recent innovation in the design of spatial interaction models. They are receiving increasing attention in recent years because of their powerful universal approximation properties. In essence they are devices for non-parametric statistical inferences, providing an elegant formalism for spatial interaction modelling. This contribution meets an urgent demand for methodological guidelines on how to develop robust applications that work from a statistical perspective. It introduces various components of a methodology for neural spatial interaction modelling that consists of a model specification framework to produce consistent estimators, a parameter estimation framework to compute parameter estimates that optimize an explicit fitness criterion and a framework to evaluate the model performance.JEL classification: C31, C45, R19

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Section: Discussionmentioning

confidence: 99%

Principles of Neural Spatial Interaction Modeling

Fischer

2009

Advances in Spatial Science

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“…It has been successfully applied on a plethora of applications [7], [8], [9], [10], [11], [12]. Like other EAs, DE can be easily parallelized [8].…”

Section: Introductionmentioning

confidence: 99%

Vector evaluated differential evolution for multiobjective optimization

Parsopoulos

Tasoulis

Pavlidis

et al.

Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)

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Abstract-A parallel, multi-population Differential Evolution algorithm for multiobjective optimization is introduced. The algorithm is equipped with a domination selection operator to enhance its performance by favoring non-dominated individuals in the populations. Preliminary experimental results on widely used test problems are promising. Comparisons with the VEGA approach are provided and discussed.

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“…All these models are members of the following general class of ( ) Fischer, Hlavackova-Schindler and Reismann 1999;Fischer and Leung 1998;Gopal and Fischer 1996;Black 1995;Fischer and Gopal 1994;Gopal and Fischer 1993;Openshaw 1993).…”

Section: The Case Of Unconstrained Spatial Interactionmentioning

confidence: 99%

A Methodology for Neural Spatial Interaction Modelling

Reismann¹

Spatial Analysis and GeoComputation

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A global search procedure for parameter estimation in neural spatial interaction modelling

Cited by 19 publications

References 0 publications

Principles of Neural Spatial Interaction Modeling

Principles of Neural Spatial Interaction Modeling

Vector evaluated differential evolution for multiobjective optimization

A Methodology for Neural Spatial Interaction Modelling

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