<abstract xmlns="http://www.w3.org/1999/xhtml"><p> The field of processing information provided by measurement results is one of the most important components of geodetic technologies. The dynamic development of this field improves classic algorithms for numerical calculations in the aspect of analytical solutions that are difficult to achieve. Algorithms based on artificial intelligence in the form of artificial neural networks, including the topology of connections between neurons have become an important instrument connected to the problem of processing and modelling processes. This concept results from the integration of neural networks and parameter optimization methods and makes it possible to avoid the necessity to arbitrarily define the structure of a network. This kind of extension of the training process is exemplified by the algorithm called the Group Method of Data Handling (GMDH), which belongs to the class of evolutionary algorithms. The article presents a GMDH type network, used for modelling deformations of the geometrical axis of a steel chimney during its operation. </p></abstract>

The field of processing information provided by measurement results is one of the most important components of geodetic technologies. The dynamic development of this field improves classic algorithms for numerical calculations in the aspect of analytical solutions that are difficult to achieve. Algorithms based on artificial intelligence in the form of artificial neural networks, including the topology of connections between neurons have become an important instrument connected to the problem of processing and modelling processes. This concept results from the integration of neural networks and parameter optimization methods and makes it possible to avoid the necessity to arbitrarily define the structure of a network. This kind of extension of the training process is exemplified by the algorithm called the Group Method of Data Handling (GMDH), which belongs to the class of evolutionary algorithms. The article presents a GMDH type network, used for modelling deformations of the geometrical axis of a steel chimney during its operation.

Elements of an algorithm for optimizing a parameter-structural neural network

Department of Land and Environment Engineering, Institute of Building Engineering, University of Zielona Góra, ul. Prof. Z. Szafrana 1, 65-516 Zielona Góra, Poland

Reports on Geodesy and Geoinformatics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Elements of an algorithm for optimizing a parameter-structural neural network"}

The field of processing information provided by measurement results is one of the most important components of geodetic technologies. The dynamic development...

Elements of an algorithm for optimizing a parameter-structural neural network

Online veröffentlicht: 14. Juli 2016

Seitenbereich: 27 - 35

DOI: https://doi.org/10.1515/rgg-2016-0019

Schlüsselwörter
neural networks, group data handling, deviations from the vertical

© 2016

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Elements of an algorithm for optimizing a parameter-structural neural network

Online veröffentlicht: 14. Juli 2016

Seitenbereich: 27 - 35

DOI: https://doi.org/10.1515/rgg-2016-0019

Schlüsselwörterneural networks, group data handling, deviations from the vertical

© 2016

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
neural networks, group data handling, deviations from the vertical