استخدام الخوارزمية الجينية لتقدير نقاط التغيير في إحدى نماذج النظم الحركية غير المستقرة مع التطبيق

الملخص EN

One of the diagnostic methods for non-stationary dynamic systems was used in this research , namely data segmentation.

The identification problem for non-stationary systems starts from the outline diagnostic, which considered as the cornerstone of reaching a model that is more appropriate in describing the system .

Consequently the data segmentation operation is performed, were the input and output series are segmented into time intervals so that stable data can be obtained within each interval.

The main objective of the data segmentation process is to minimize the loss function and obtain a model that represents the data interval in question as best as possible.

We used the genetic algorithm to find the number, location change points, and model fits for each part by applying a generator.

Results showed that it is a good and efficient way to estimate the location and number of points of change.