Title :
Evolutionary system identification and control
Author_Institution :
Orincon Corp., San Diego, CA, USA
Abstract :
Evolutionary optimization is proposed as a method for machine learning. Simulating evolution can be used for the prediction, identification, and control of time-varying plants. Models which describe the input-output characteristics of the system are evolved in fast time. This evolutionary programming can address systems in which there is little or no prior knowledge. There is no requirement for using a squared error or other smooth criterion. The technique is more versatile than classic prediction and correlation error methods
Keywords :
artificial intelligence; identification; learning systems; optimisation; evolutionary optimization; evolutionary programming; input-output characteristics; machine learning; time-varying plants; Control systems; Design optimization; Least squares approximation; Machine learning; Mathematical model; Optimization methods; Parameter estimation; Predictive models; System identification; Yield estimation;
Conference_Titel :
Industrial Electronics Society, 1990. IECON '90., 16th Annual Conference of IEEE
Conference_Location :
Pacific Grove, CA
Print_ISBN :
0-87942-600-4
DOI :
10.1109/IECON.1990.149320
