Evolutive Neural Net Fuzzy Filtering: Basic Description

The paper describes the operation principles of the evolutive neuro fuzzy filtering (ENFF) properties, which based on back propagation fuzzy neural net, this filter adaptively choose and emit a decision according with the reference signal changes of an external reference process, in order to actualize the best correct new conditions updating a process. This neural net fuzzy filter mechanism selects the best parameter values into the knowledge base (KB), to update the filter weights giving a good enough answers in accordance with the reference signal in natural sense. The filter 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 filtering process states require that bound into its own time limit as real time system, considering the Nyquist and Shannon criteria. The characterization of the membership functions builds the knowledge base in probabilistic sense with respect to the rules set inference to describe the reference system and deduce the new filter decision, performing the ENFF answers. Moreover, the paper describes schematically the neural net architecture and the decision-making stages in order to integrate them into the filter architecture as intelligent system. The results expressed in formal sense use the concepts into the paper references with a simulation of the ENFF into a Kalman filter structure using the Matlab tool.