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

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


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.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • Czaja J. (1983). Geodezja inżynieryjno - przemysłowa. Skrypt Uczelniany nr 893 Akademii Górniczo - Hutniczej w Krakowie. Kraków.

  • Duch W., Korbicz J., Rutkowski L., Tadeusiewicz R. (2000). Sieci neuronowe. Biocybernetyka i inżynieria biomedyczna, tom 6. Akademicka Oficyna Wydawnicza EXIT. Warszawa.

  • Gocał J. (2010). Geodezja inżynieryjno - przemysłowa, tom III. Wydawnictwo Akademii Górniczo - Hutniczej. Kraków.

  • Iwachnienko A.G., (1971). Polynominal theory of complex systems. IEEE Trans. Systems, Man and Cybernetics, Vol.SMC-1, No.4.

  • Iwachnienko A.G., (1982). Induktiwnyj metod samoorganizacji modelej słożonych system. Naukowa Dumka. Kijów.

  • Korbicz J. (2009). Sztuczne sieci neuronowe i ich zastosowanie w elektrotechnice i energetyce. Przegląd Elektroniczny R. 85, Nr 9. Warszawa.

  • Luzar M. (2010). Przybornik modelowania neuronowego GMDH. Conference Archives PTETiS Vol. 28.


Journal + Issues