@misc{Oh_Sung-Kwun_The_2001,
 author={Oh, Sung-Kwun and Pedrycz, Witold (1953– )},
 copyright={Creative Commons Attribution BY 4.0 license},
 address={Warszawa},
 journal={Raport Badawczy = Research Report},
 howpublished={online},
 year={2001},
 publisher={Instytut Badań Systemowych. Polska Akademia Nauk},
 publisher={Systems Research Institute. Polish Academy of Sciences},
 language={eng},
 abstract={In this study, a class of neural arhitectures of polynomial neural networks (PNNs) was introduced and investigated. A comprehensive design methodology is discussed. A series of numeric experiments were carried out. PNN is a flexible neural architecture whose structure (topology) is deveoped through learining. The number of layers of the PNN is not fixes in advance but is generated on the fly. In this sense, PNN is a self-organizing network. The essence of the design procedure dwells on the Group Method of Data handling (GMDH). Each node of the PNN exhibits a high level of flexibility and realizes a polynomial type of mapping (linear, quadratic, and cubic) between input and output variables. The experimental part of the study involves two representative time series such as Box-Jenkins gas furnace data and a pH netralization process.},
 title={The design of self - organizing polynomial neural networks},
 type={Text},
 URL={http://www.rcin.org.pl/Content/129950/PDF/RB-2001-58.pdf},
 keywords={Time series, Polynomial neural networks, Group method of data handking (gmdh), Design procedure, High-order polynomial, Multi-variable systems, Proces projektowania, Szereg czasowy},
}