9 plot_ model

Purpose

Displays the optimal filter and the solution achieved by the adaptive filter for evaluating the adaptive system identification applications.

Syntax

plot_model(w,h,e)

Description

plot_model() is helpful in the verification stage in adaptive system identification applications. plot_model() takes as input the adaptive filter coefficients vector $w(n)$, the impulse response of the system to be modeled $h$, and the mean square error vector $e(n)$ (that is usually returned by update_ipwin()), and returns after rendering the graph window. The variables of plot_model() are summarized below.

 
Input variables:
   w : estimated impulse response
   h : actual impulse response
   e : estimation error history

Example

Figure 9.8: The modeling (system identification) graph window.

An example graph window generated using plot_model() is shown in Fig. 9.8. The top left panel shows the impulse response of the optimal solution $w_{opt}$ for the system identification problem, which is the impulse response $h$ in this case. The frequency response of this optimal solution is plotted in the middle left panel. The top two right panels show the impulse response and frequency response of the adaptive model. The bottom left panel displays the evolution of the mean square error with time and the bottom right panel displays the difference between the optimum solution coefficients and the adaptive model coefficients.