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Some Learning Methods in Functional Networks
Author(s) -
Castillo Enrique,
Gutiérrez José Manuel,
Cobo Angel,
Castillo Carmen
Publication year - 2000
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/0885-9507.00205
Subject(s) - minimax , computer science , separable space , selection (genetic algorithm) , network topology , artificial intelligence , model selection , algorithm , topology (electrical circuits) , theoretical computer science , mathematical optimization , machine learning , mathematics , mathematical analysis , combinatorics , operating system
This article is devoted to learning functional networks. After a short introduction and motivation of functional networks using a CAD problem, four steps used in learning functional networks are described: (1) selection of the initial topology of the network, which is derived from the physical properties of the problem being modeled, (2) simplification of this topology, using functional equations, (3) estimation of the parameters or weights, using least squares and minimax methods, and (4) selection of the subset of basic functions leading to the best fit to the available data, using the minimum‐description‐length principle. Several examples are presented to illustrate the learning procedure, including the use of a separable functional network to recover the missing data of the significant wave height records in two different locations, based on a complete record from a third location where the record is complete.