Evolutive neural fuzzy filtering: An approach

The paper, is a description of the evolutive neural fuzzy filtering with real time conditions, giving the basics of its operation based on a back propagation fuzzy neural net, which adaptively choose and emit a decision according with the reference signal changes in order to loop the correct new conditions for a process. This work is an approach about the operation of the evolutive neural fuzzy digital filters (ENFDF). Using the neural fuzzy mechanism select the best parameter values into the knowledge base (KB), updating the filter weights to give a good answers with respect to the desired signal in natural linguistic sense. Additionally, the filtering architecture includes a decision making stage using an inference into its structure to deduce the filter decisions in accordance with the previous and actual filter answer in order to updates the new decision with respect to the new reference system conditions. The process requires that all of its states bound into ENFDF time limit as a real time system. In this paper, the characterization of the membership functions building the knowledge base in a probabilistic way with respect to the rules set in order to describe the reference system and the inference to selects the new filter decision. Moreover, the work describes in schematic sense the neural net architecture with the decision-making stages in order to integrate the filtering stages as an evolutive system. The results expressed in formal sense using the concepts into the paper references. Finally, we present the simulation of the ENFDF operation using the Matlab