Symbolic function network.

Full Abstract

In this paper a model called symbolic function network (SFN) is introduced; that is based on using elementary functions (for example powers, the exponential function, and the logarithm) as building blocks. The proposed method uses these building blocks to synthesize a function that best fits the training data in a regression framework. The resulting network is of the form of a tree, where adding nodes horizontally means having a summation of elementary functions and adding nodes vertically means concatenating elementary functions. Several new algorithms were proposed to construct the tree based on the concepts of forward greedy search and backward greedy search, together with applying the steepest descent concept. The method is tested on a number of examples and it is shown to exhibit good performance.

Author information

Author/s: Eskander, George S (GS); Atiya, Amir F (AF);

Affiliation: Department of Computer Engineering, Baha Private College of Science, Al Baha, Saudi Arabia. georgeeskander(-atsign-)yahoo.com

Journal and publication information

Publication Type: Journal Article

Journal: Neural networks : the official journal of the International Neural Network Society (Neural Netw), published in United States. (Language: eng)

Reference: 2009-May; vol 22 (issue 4) : pp 395-404

Dates: Created 2009/05/25; Completed 2009/08/21;

PMID: 19342195, status: MEDLINE (last retrieval date: 8/21/2009, IMS Date: )

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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