مقارنة الخوارزمية الجينية مع طريقتي المربعات الصغرى اللاخطية و الإمكان الأعظم لتقدير انموذج Boxbod اللاخطي باستخدام المحاكاة

Abstract EN

In this research, one of the nonlinear regression models is studied, which is BoxBOD, which is characterized by nonlinear parameters, as the difficulty of this model lies inestimating its parameters for being nonlinear, as its parameters were estimated by sometraditional methods, namely the method of non-linear least squares and the greatestpossible method and one of the methods of artificial intelligence, it is a genetic algorithm, as this algorithm was based on two types of functions, one of which is the function of thesum of squares of error and the second is the function of possibility.

For comparisonbetween the methods used in the research, the comparison scale was based on the averageerror squares, and for the purpose of data generation, five linear models were used assimulation models.

The results of the first four models showed that the non-linear leastsquares method outperformed the rest of the methods used in the research.

As for theresults of the fifth simulated model, the genetic algorithm based on the function ofpossibility overtook the rest of the methods.