A genetic based approach to the type I structure identification problem

15Citations
Citations of this article
6Readers
Mendeley users who have this article in their library.

Abstract

The problem of system input selection, dubbed in the literature as Type I Structure Identification problem, is addressed in this paper using an effective novel method. More specifically, the fuzzy curve technique, introduced by Lin and Cunningham (1995), is extended to an advantageous fuzzy surface technique; the latter is used for fast building a coarse model of the system from a subset of the initial candidate inputs. A simple genetic algorithm, enhanced with a local search operator, is used for finding an optimal subset of necessary and sufficient inputs by considering jointly more than one inputs. Extensive simulation results on both artificial data and real world data have demonstrated comparatively the advantages of the proposed method. © 2005 Institute of Mathematics and Informatics, Vilnius.

References Powered by Scopus

Fuzzy classifications using fuzzy inference networks

10281Citations
N/AReaders
Get full text

Fuzzy Logic in Control Systems: Fuzzy Logic Controller—Part I

4105Citations
N/AReaders
Get full text

Using Mutual Information for Selecting Features in Supervised Neural Net Learning

2169Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Medical image protection using genetic algorithm operations

71Citations
N/AReaders
Get full text

Piecewise-linear approximation of non-linear models based on probabilistically/possibilistically interpreted intervals' numbers (INs)

54Citations
N/AReaders
Get full text

Iterated tabu search for the unconstrained binary quadratic optimization problem

53Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Papadakis, S. E., Tzionas, P., Kaburlasos, V. G., & Theocharis, J. B. (2005). A genetic based approach to the type I structure identification problem. Informatica, 16(3), 365–382. https://doi.org/10.15388/informatica.2005.104

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 4

80%

Professor / Associate Prof. 1

20%

Readers' Discipline

Tooltip

Computer Science 3

50%

Engineering 3

50%

Save time finding and organizing research with Mendeley

Sign up for free