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- W205083280 abstract "Parameter estimation is one of the central issues in neural spatial interaction modelling.Current practice is dominated by gradient based local minimization techniques. Theyfind local minima efficiently and work best in unimodal minimization problems, butcan get trapped in multimodal problems. Global search procedures provide analternative optimization scheme that allows to escape from local minima. Differentialevolution has been recently introduced as an efficient direct search method foroptimizing real-valued multi-modal objective functions (Storn and Price 1997). Themethod is conceptually simple and attractive, but little is known about its behaviour inreal world applications. This paper explores this method as an alternative to currentpractice for solving the parameter estimation task, and attempts to assess ist robustness,measured in terms of in-sample and out-of-sample performance. A benchmarkcomparison against backpropagation of conjugate gradients is based on Austrianinterregional telecommunication traffic data. (authors' abstract)" @default.
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- W205083280 date "1998-09-01" @default.
- W205083280 modified "2023-09-23" @default.
- W205083280 title "A Gobal Search Procedure forParameter Estimation inNeural Spatial Interaction Modelling" @default.
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