Evolutive Neural Fuzzy Filtering: Real Time Constrains J. J. Medel Juárez, J. C. García Infante, J. C. Sánchez García Microelectronics Research Department Professional School of Mechanical and Electrical Engineering Av. Santa Ana No. 1000, Col. San Francisco Culhuacan Del. Coyoacan, México D.F. Centre of Computing Research, National Polytechnic Institute Vallejo D. F. 07738, México jjmedelj@yahoo.com.mx, jcnet21@yahoo.com, jcsanchezgarcia@gmail.com Abstract: - In this paper, we describe the evolutive neural fuzzy filtering properties with real time conditions, giving the principles 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 evolutive neural fuzzy digital filters (ENFDF). This filter using the neural fuzzy mechanism select the best parameter values into the knowledge base (KB), actualizing the filter weights to give a good enough answers with respect to the reference 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. This process requires that all of its states bound into ENFDF time limit as a real time system, considering the Nyquist and Shannon criteria. In this paper, we characterize the membership functions building the knowledge base in a probabilistic way with respect to the rules set inference to describe the reference system and the inference to deduce the new filter decision, performing the ENFDF. Moreover, the paper 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 © software. The paper has eight sections conformed as follows: 1. Introduction, 2. Filtering conditions, 3. Neural net description, 4. Rule base strategy, 5. Real time scheme, 6. Simulations, 7.Conclusion and References. Key-Words: - Digital filters, neural net, evolutive systems, fuzzy logic, inference mechanism, real time. 1 Introduction There are some problems in the development of a system as restrictions into its capacity to model high evolutive changing processes. The new systems should use into its architecture evolutive tools in order to get its own perception and give the best decision answers, using this to solve complex problems, actualizing and adapting its perceptions and answers in accordance with a reference dynamical system. The use of learning techniques based on searching the optimal solutions represents a classical alternative manner to obtain knowledge. The evolutive systems into the neural fuzzy digital filtering is an option for to obtain different decision answer levels, in accordance with a system dynamics or reference model, adapting its answers to the possible changes by selecting the best values in order to get the necessary convergence conditions, which should has the best operation at each time. An evolutive neural fuzzy digital filter has a solution searching process based on the reference model conditions having at each moment a regulation inference mechanism that describes the desire conditions, selecting the best answer into the knowledge base in accordance with it, emitting the new answer condition for the best adaptation. Proceedings of the 8th WSEAS International Conference on SIGNAL PROCESSING, ROBOTICS and AUTOMATION ISSN: 1790-5117 221 ISBN: 978-960-474-054-3