Functional Networks

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Author(s): Oscar Fontenla-Romero (University of A Coruña, Spain), Bertha Guijarro-Berdiñas (University of A Coruña, Spain)and Beatriz Pérez-Sánchez (University of A Coruña, Spain)
Copyright: 2009
Pages: 10
Source title: Encyclopedia of Artificial Intelligence
Source Author(s)/Editor(s): Juan Ramón Rabuñal Dopico (University of A Coruña, Spain), Julian Dorado (University of A Coruña, Spain)and Alejandro Pazos (University of A Coruña, Spain)
DOI: 10.4018/978-1-59904-849-9.ch101

Keywords: Artificial Intelligence / Computer Science & IT / Information Science Reference

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Abstract

Functional networks are a generalization of neural networks, which is achieved by using multiargument and learnable functions, i.e., in these networks the transfer functions associated with neurons are not fixed but learned from data. In addition, there is no need to include parameters to weigh links among neurons since their effect is subsumed by the neural functions. Another distinctive characteristic of these models is that the specification of the initial topology for a functional network could be based on the features of the problem we are facing. Therefore knowledge about the problem can guide the development of a network structure, although on the absence of this knowledge always a general model can be used. In this article we present a review of the field of functional networks, which will be illustrated with practical examples.

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