Title :
Evolutionary programming for minimum uncertainty parameter estimation for linear models
Author :
Belforte, Gustavo ; Gay, Paolo
Author_Institution :
Dipt. di Autom. e Inf., Politecnico di Torino, Italy
Abstract :
In this paper linear parameter models with stochastic and set membership error descriptions are considered. A genetic algorithm is presented for the determination of a fixed size measurement set that allows one to avert the case of unidentifiability and permits to estimate the system parameters with the smallest uncertainty of the parameters. Such an approach is relevant in experimental design when an optimal sampling schedule is sought. A numerical simulation study has been carried out to evaluate the performances of the algorithm
Keywords :
design of experiments; genetic algorithms; linear systems; parameter estimation; set theory; evolutionary programming; genetic algorithm; linear models; parameter estimation; parameter uncertainty; set theory; Design for experiments; Genetic algorithms; Genetic programming; Numerical simulation; Parameter estimation; Performance evaluation; Sampling methods; Size measurement; Stochastic processes; Uncertainty;
Conference_Titel :
Control Applications, 1998. Proceedings of the 1998 IEEE International Conference on
Conference_Location :
Trieste
Print_ISBN :
0-7803-4104-X
DOI :
10.1109/CCA.1998.721663
