Parametric complexity reduction of the Meixner-like model using genetic algorithms

This article focuses on modeling and identification of a LTI system represented by Meixner-like functions. The use of the Meixner-like functions is more suitable than the use of Laguerre functions and Kautz functions especially when the system have a slow initial onset or delay. Thus, the order of the model can be low, if the Meixner-like pole is set to its optimal value. Therefore we propose, from input/output measurements, a new method; based on Genetic Algorithms, to estimate the optimal value of the Meixner-like pole. A numerical simulation shows the efficiency of the approach.