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
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;
Conference_Titel :
American Control Conference, 1992
Conference_Location :
Chicago, IL, USA
Print_ISBN :
0-7803-0210-9
