Title :
An adaptive function identification system
Author :
Jiang, Mingda ; Wright, Alden H.
Author_Institution :
Unidata Inc., Denver, CO, USA
Abstract :
Given data in the form of a collection of (x,y) pairs of real numbers, the symbolic function identification problem is to find a functional model of the form y=f(x) that fits the data. This paper describes an adaptive system for solution of symbolic function identification problems that combines a genetic algorithm and the Levenberg-Marquardt nonlinear regression algorithm. The genetic algorithm uses an expression-tree representation rather than the more usual binary-string representation. Experiments were run with data generated using a wide variety of function models. The system was able to find a function model that closely approximated the data with a very high success rate
Keywords :
adaptive systems; genetic algorithms; learning (artificial intelligence); Levenberg-Marquardt nonlinear regression algorithm; adaptive function identification system; adaptive system; expression-tree representation; genetic algorithm; symbolic function identification problem; Adaptive systems; Artificial intelligence; Computer science; Design methodology; Design optimization; Genetic algorithms; Intrusion detection; Learning systems; Machine learning; Regression analysis;
Conference_Titel :
Developing and Managing Intelligent System Projects, 1993., IEEE International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-8186-3730-7
DOI :
10.1109/DMISP.1993.248637
