DocumentCode
489327
Title
An Approach to Constrained Neural Global Optimization
Author
Adamczy, B. ; Zohdy, M.A.
Author_Institution
Center for Robotics and Advanced Automation, Oakland University, Rochestes, MI 48309
fYear
1992
fDate
24-26 June 1992
Firstpage
196
Lastpage
201
Abstract
This paper presents a stochastic neural approach to the problem of determining the global extremum of multivariable, non-linear objective functions subject to constraints. The approximate value of the global extremum is found by using a special transformation followed by neural least squares estimation.
Keywords
Constraint optimization; Equations; Least squares approximation; Least squares methods; Neural networks; Q measurement; Robots; Stochastic processes; Tellurium; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
American Control Conference, 1992
Conference_Location
Chicago, IL, USA
Print_ISBN
0-7803-0210-9
Type
conf
Filename
4792054
Link To Document