Detecting constructions of nonlinear integral systems from input-output data: an application of neural networks

Author

Wang, Jia ; Wang, Zhenyuan

Author_Institution

Dept. of Comput. Sci., State Univ. of New York, Binghamton, NY, USA

fYear

1996

fDate

19-22 Jun 1996

Firstpage

559

Lastpage

563

Abstract

If the input-output relation of a multi-input system can be represented by some kind of integral with respect to a nonnegative monotone set function, which is not necessarily additive, then the construction of the system may be entirely described by the monotone set function. After obtaining input-output data from such a system, the set function can be optimally determined by using a specially designed neural network algorithm

Keywords

functions; fuzzy set theory; integration; mathematics computing; neural nets; nonlinear systems; optimisation; Choquet integral; constrained optimization; gradient method; input-output data; least-square method; multi-input system; neural network algorithm; nonadditive function; nonlinear integral system construction detection; nonlinear system; nonnegative monotone set function; optimal function determination; Algorithm design and analysis; Application software; Computer science; Data engineering; Electronic mail; Fuzzy sets; Industrial engineering; Neural networks; Nonlinear systems; Particle measurements;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Information Processing Society, 1996. NAFIPS., 1996 Biennial Conference of the North American

Conference_Location

Berkeley, CA

Print_ISBN

0-7803-3225-3

Type

conf

DOI

10.1109/NAFIPS.1996.534796

Filename

534796