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Welcome to the POLYNOMIAL NEURAL NETWORKs!

start the program

mirror connection

The Polynomial Neural Network (PNN) algorithm[1,2]  is also known as Iterational Algorithm of Group Methods of Data Handling (GMDH). GMDH were originally proposed by Prof. A.G. Ivakhnenko. PNN correlates input and target variables using (non) linear regression. In this particular software the user can define the desired properties of the solution such as the number of terms and the maximum degree of polynoms using an approach proposed by Prof. Yu.P.Yurachkovsky[3].

This software was  developed by I. V. Tetko, T.I. Aksenova and V.V. Volkovich when one of the authors (IVT) was working in the University of Lausanne. The visualization of results is done using Java code developed by  D.V. Filipov. The data input format is described here.

Acknowledgment This software was developed with partial financial support from INTAS and University of Lausanne.

References
  1. Aksyonova, T. I.; Volkovich, V. V.; Tetko, I. V. Robust Polynomial Neural Networks in Quantitative-Structure Activity Relationship Studies, SAMS, 2003, 43, 1331-1339, article.

  2. Tetko, I. V.; Aksenova, T. I.; Volkovich, V. V.; Kasheva, T. N.; Filipov, D. V.; Welsh, W. J.; Livingstone, D. J.; Villa, A. E. P. Polynomial neural network for linear and non-linear model selection in quantitative-structure activity relationship studies on the internet, SAR QSAR Environ. Res., 2000, 11, 263-80, article.

  3. Yurachkovsky, Y.P. "Restoration of Polynomial  Dependencies Using Self-Organization." Soviet Automatic Control, 1981, 14, 17-22.

 

 

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