Title :
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
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;
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
DOI :
10.1109/NAFIPS.1996.534796
