You are here: Wiki>AI_GEOSTATS Web>AI_GEOSTATSPapers>Papers20100616125928 (13 Aug 2010, theresiafreska)Edit Attach
Title: Parameter Estimation in Neural Spatial Interaction Modelling by a Derivative Free Global Optimization Method

Date: 25 July 1999

Authors: Manfred M. Fischer, Martin Reismann, Katerina Hlavácková-Schindler

Link: http://www.geovista.psu.edu/sites/geocomp99/Gc99/007/gc_007.htm

Abstract:

Parameter estimation is one of the central issues in neural spatial interaction modelling. Current practice is dominated by gradient-based local minimization techniques. They find local minima efficiently and work best in unimodal minimization problems, but can get trapped in multimodal problems. Global search procedures provide an alternative optimization scheme that allows escape from local minima. Differential evolution recently has been introduced as an efficient direct search method for optimizing real-valued multi-modal objective functions (Storn and Price 1997). The method is conceptually simple and attractive, but little is known about its behaviour in real-world applications. This paper explores this method as an alternative to current practice for solving the parameter estimation task, and attempts to assess its robustness, measured in terms of in-sample and out-of-sample performance. A benchmark comparison against back propagation of conjugate gradients is based on Austrian interregional telecommunication traffic data.

Reference:

IV International Conference on GeoComputation, Mary Washington College, Fredericksburg, VA, USA, 25-28 July 1999.
Topic revision: r4 - 13 Aug 2010 18:16:16, theresiafreska
This site is powered by FoswikiCopyright &© by the contributing authors. All material on this collaboration platform is the property of the contributing authors.
Ideas, requests, problems regarding Wiki? Send feedback