ISSN:
1573-0409
Keywords:
Model identification
;
static nonlinear systems
;
feedforward neural networks
;
structure identification
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
,
Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
Notes:
Abstract System identification can be divided into structure and parameter identification. In most system-identification approaches the structure is presumed and only a parameter identification is performed to obtain the coefficients in the functional system. Yet, often there is little knowledge about the system structure. In such cases, the first step has to be the identification of the decisive input variables. In this paper a black-box input variable identification approach using feedforward neural networks is proposed.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF00449705
Permalink