Abstract
Building a feedforward computational neural network model (CNN) involves two distinct
tasks: determination of the network topology and weight estimation. The specification of a
problem adequate network topology is a key issue and the primary focus of this contribution.
Up to now, this issue has been either completely neglected in spatial application domains, or
tackled by search heuristics (see Fischer and Gopal 1994). With the view of modelling
interactions over geographic space, this paper considers this problem as a global
optimization problem and proposes a novel approach that embeds backpropagation learning
into the evolutionary paradigm of genetic algorithms. This is accomplished by interweaving a
genetic search for finding an optimal CNN topology with gradient-based backpropagation
learning for determining the network parameters. Thus, the model builder will be relieved of
the burden of identifying appropriate CNN-topologies that will allow a problem to be solved
with simple, but powerful learning mechanisms, such as backpropagation of gradient descent
errors. The approach has been applied to the family of three inputs, single hidden layer,
single output feedforward CNN models using interregional telecommunication traffic data for
Austria, to illustrate its performance and to evaluate its robustness. (authors' abstract)
tasks: determination of the network topology and weight estimation. The specification of a
problem adequate network topology is a key issue and the primary focus of this contribution.
Up to now, this issue has been either completely neglected in spatial application domains, or
tackled by search heuristics (see Fischer and Gopal 1994). With the view of modelling
interactions over geographic space, this paper considers this problem as a global
optimization problem and proposes a novel approach that embeds backpropagation learning
into the evolutionary paradigm of genetic algorithms. This is accomplished by interweaving a
genetic search for finding an optimal CNN topology with gradient-based backpropagation
learning for determining the network parameters. Thus, the model builder will be relieved of
the burden of identifying appropriate CNN-topologies that will allow a problem to be solved
with simple, but powerful learning mechanisms, such as backpropagation of gradient descent
errors. The approach has been applied to the family of three inputs, single hidden layer,
single output feedforward CNN models using interregional telecommunication traffic data for
Austria, to illustrate its performance and to evaluate its robustness. (authors' abstract)
| Originalsprache | Englisch |
|---|---|
| Erscheinungsort | Vienna |
| Herausgeber | WU Vienna University of Economics and Business |
| DOIs | |
| Publikationsstatus | Veröffentlicht - 1 Feb. 1998 |
Publikationsreihe
| Reihe | Discussion Papers of the Institute for Economic Geography and GIScience |
|---|---|
| Nummer | 61/98 |
WU Working Paper Reihe
- Discussion Papers of the Institute for Economic Geography and GIScience