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 in estimating its parameters for being nonlinear, as its parameters were estimated by some traditional methods, namely the method of non-linear least squares and the greatest possible 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 the sum of squares of error and the second is the function of possibility. For comparison between the methods used in the research, the comparison scale was based on the average error squares, and for the purpose of data generation, five linear models were used as simulation models. The results of the first four models showed that the non-linear least squares method outperformed the rest of the methods used in the research. As for the results of the fifth simulated model, the genetic algorithm based on the function of possibility overtook the rest of the methods.
مقارنة الخوارزمية الجينية مع طريقتي المربعات الصغرى اللاخطية والامكان الاعظم اللاخطي باستخدام المحاكاة لتقدير انموذج BoxBOD اللاخطي باستخدام المحاكاة
Publication Date
Fri May 01 2020
Journal Name
مجلة بحوث الشرق الأوسط
