Fuzzy neural net Digital Filtering: A MIMO structure

The paper is an analysis of the neural fuzzy filtering with multivariate description and realtime properties in order to give the elements of this kind of filters called RTMNFDF (Real Time Multivariate Neuro-Fuzzy Digital Filtering). This kind of MIMO (multiple inputs and multiple outputs) filter uses an adaptive inference mechanism into its own architecture with a fuzzy neural net structure to get the best answers in accordance to the corresponding parameter matrix values from the knowledge base (KB); actualizing the filter weights to give the answers in natural linguistic sense. The advantage with respect to classical filtering methods, is that dynamically deduce the reference system conditions and take a decision from its knowledge base in accordance with the desired signal at the filter input giving the best answer through the time. The filter structure requires that all of the state sequences bound into RTMNFDF time limits as a real-time system considering the Nyquist and Shannon criterion. The paper describes the characterization of the membership functions into the knowledge base with probabilistic description with respect to the rules set decisions, performing the RTFNDF. In addition, the paper shows schematically the structure of the neural net into the filter description. The results formally integrate the concepts exposed in the paper references. Finally, into the simulation show illustratively the RTMNFDF operations graphics using as a tool the Matlab^®software. The paper has eight sections conformed as follows: 1. Introduction, 2. Neural fuzzy description, 3. Parameter properties, 4. Filter mechanism, 5. Neural net architecture, 6. Real time descriptions, 7. Results, Conclusions and References.