Real-time neuro-fuzzy digital filtering: Basic concepts

In this paper we describe the neural fuzzy filtering properties in real-time sense; giving an approach about the real-time neuro-fuzzy digital filters, defined in acronym form as RTNFDF. This kind of filters require the adaptive inference mechanism into the fuzzy logic structure to deduce the filter answers in order to select the best parameter values into the knowledge base (KB), actualizing the filter weights to give a good enough answers in natural linguistic sense; this require that all of the states bound into RTNFDF time limit as a real-time system, considering the Nyquist criteria. In this paper we characterize the membership functions into the knowledge base in a probabilistic way respect to the rules set decisions without lost its real-time description, performing the RTFNDF. Moreover, the paper describes in schematic sense the neurons set architecture into the filter description. The results expressed in formal sense use the concepts exposed in the papers included into the references. Finally, we present in illustrative manner the RTNFDF operations using as a tool the Matlab^© software. Explicitly, the paper has eight sections conformed as follows: 1. Introduction, 2. Neural Architecture, 3. Rule Base Dynamics, 4. Neural Rules, 5. Real-time Descriptions, 6. Restrictions for RTNFDF, 7. Simulation, Conclusions and References.