A new algorithm for estimation of parameter of nonlinear regression modeling
The authors consider an identification problem for estimating the parameter vector in a nonlinear regression model. They first reduce the above estimation problem to minimizing a nonlinear least squares cost functional and then propose a hybrid genetic algorithm to solve the above optimization problem. The proposed algorithm can rapidly converge to the global optima by performing global searches and local searches alternatively in the parameter space. The simulation example is given to illustrate the general use and the effectiveness of their algorithm.
scientific article; zbMATH DE number 1695308