Metadata language
The design of self - organizing polynomial neural networks
Subtitle:Raport Badawczy = Research Report ; RB/58/2001
Creator:Oh, Sung-Kwun ; Pedrycz, Witold (1953– )
Publisher:Instytut Badań Systemowych. Polska Akademia Nauk ; Systems Research Institute. Polish Academy of Sciences
Place of publishing: Date issued/created: Description:40 pages ; 21 cm ; Bibliography p. 37-40
Subject and Keywords:Time series ; Polynomial neural networks ; Group method of data handking (gmdh) ; Design procedure ; High-order polynomial ; Multi-variable systems ; Proces projektowania ; Szereg czasowy
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.
Relation:Raport Badawczy = Research Report
Resource type: Detailed Resource Type: Source: Language: Language of abstract: Rights:Creative Commons Attribution BY 4.0 license
Terms of use:Copyright-protected material. [CC BY 4.0] May be used within the scope specified in Creative Commons Attribution BY 4.0 license, full text available at: ; -
Digitizing institution:Systems Research Institute of the Polish Academy of Sciences
Original in:Library of Systems Research Institute PAS
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