Gaussian clarification based on sign function

This paper presents a clarification model in the fuzzy sense based on the Membership Inverse Function (MIF), in Control Theory. It is considered as an identification and requires bounded input and output signals. The sign function and its derivative is regarded as a Gaussian function into the mathem...

Full description

Autores:
Medel, José De Jesús
Espinosa-Santiago, Mario
Fernández-Muñoz, José Luis
Tipo de recurso:
Article of journal
Fecha de publicación:
2016
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/60459
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/60459
http://bdigital.unal.edu.co/58791/
Palabra clave:
62 Ingeniería y operaciones afines / Engineering
Clarification
Fuzzy Logic
Identification
Stochastic Process
Clarificación
Lógica Difusa
Identificación
Proceso Estocástico
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
Description
Summary:This paper presents a clarification model in the fuzzy sense based on the Membership Inverse Function (MIF), in Control Theory. It is considered as an identification and requires bounded input and output signals. The sign function and its derivative is regarded as a Gaussian function into the mathematical Membership description. Specifically, the sign function considers the difference between the absolute state variable values and its centroid, rather than remaining in the triangle inequality. Therefore, the theoretical result applied in Matlab® using the reference values as an identification process in an Auto Regressive Moving Average (ARMA) (1, 1) model describes the performance. The clarification converging in almost all points of the desired signal depends on the different initial conditions. The convergence obtained by the functional error built by the second probability moment was also used and applied in the same software giving an illustrative description.