Juan Carlos García Infante

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 con-ditions. The filtering process states require that bound into its own time limit as real time system, considering the Ny-quist and Shannon criteria. The characterization of the membership functions builds the knowledge base in probabilis-tic sense with respect to the rules set inference to describe the reference system and deduce the new filter decision, per-forming the ENFF answers. Moreover, the paper describes schematically the neural net architecture and the deci-sion-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.

Execution time Validation of the mathematical models of a electric motor in Soft Real Time simulation

This paper presents the simulation and comparison of models of first and second order of an electrical machine, in Soft Real Time. First, it describes the real-time systems and their classification. After is considered the electromechanical circuit of a Series wound DC motor, for obtaining the first and second order equations which represent the system (In continuous time and discrete time). The models obtained are simulated in SIMULINK and their parameters (electric current and angular speed) are compared between them for obtaining the relative error. Once compared the responses of the models, the execution time of the finite difference models is obtained and compared with data acquisition device execution time.

Fuzzy Digital Filtering: Signal Interpretation

The paper makes a description of the fuzzy filter properties considering its operational principles. A digital filter interacts with a reference model signal into real process in order to get the best corresponding answer, having the minimum error at the filter output using the mean square criterion. Adding into this filter structure a fuzzy mechanism, to obtain an intelligent filtering because adaptively select and emit a decision answer according with the external reference signal changes, in order to actualize the best correct new conditions updating a process dynamically. The interpretation of the input signal level describes the operation of the reference model, to update the filter weights giving the answers approximation in accordance with the reference signal in natural form. Finally the paper shows the simulations results of the fuzzy filter into the Kalman structure using the Matlab© tool.

Fuzzy digital filter type III

A digital filter interacts with a reference model signal into a real process in order to obtain the best corresponding answer, having the minimum filter output error using the mean square criterion. A fuzzy mechanism into the filter structure was added obtaining an intelligent filter, selecting and emitting an answer decision according to the external reference signal changes. This is in order to actualize the best correct new conditions updating a dynamic process. The fuzzy filter makes an interpretation of the input signal level to select the best parameter values from a set of membership values into the knowledge base (KB), updating the filter weights giving the approximation answers according to the reference signal. This fuzzy stage improves the filter answers, minimizing the filter error criterion with a classification of its operation levels. The filtering process requires that all answers of the error criteria are probabilistically bounded, considering the Nyquist and Shannon assumptions, having the fuzzy filter simulations using Matlab© tools into the Kalman structure.