the best corresponding answer, having the minimum filter output error using the mean square criterion.
A fuzzy mechanism into the filter structure was added obtaining an intelligent filter, selecting and emit-
ting an answer decision according to the external reference signal changes. This is in order to actualize
the best correct new conditions updating a dynamic process. The fuzzy filter makes an interpretation of
the input signal level to select the best parameter values from a set of membership values into the knowl-
edge base (KB), updating the filter weights giving the approximation answers according to the reference
signal. This fuzzy stage improves the filter answers, minimizing the filter error criterion with a classifi-
cation of its operation levels. The filtering process requires that all answers of the error criteria are prob-
abilistically bounded, considering the Nyquist and Shannon assumptions, having the fuzzy filter simu-
lations using Matlab
tools into the Kalman structure.