A Study on the Evolutionary Adaptive Defuzzification Methods in Fuzzy Modeling

نویسندگان

  • Oscar Cordón
  • Francisco Herrera
  • Francisco Alfredo Márquez
  • Antonio Peregrín
چکیده

Evolutionary Adaptive Defuzzification Methods are a kind of defuzzification methods based on using a parametrical defuzzification expression tuned with evolutionary algorithms. Their goal is to increase the accuracy of the fuzzy system without loosing its interpretability. They induce a kind of rule cooperation in the defuzzification interface. 1 This paper deals with Evolutionary Adaptive Defuzzification Methods. We study their common general expression, the different defuzzification methods that can be obtained from it, their interpretation, and their accuracy. We consider two applications in order to analyse their accuracy in practice. We get some useful results for practical fuzzy systems designed by means of this kind of Intelligent Hybrid System.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Rule Base and Inference System Cooperative Learning of Mamdani Fuzzy Systems with Multiobjective Genetic Algorithms

In this paper, we present an evolutionary multiobjective learning model achieving positive synergy between the Inference System and the Rule Base in order to obtain simpler, more compact and still accurate linguistic fuzzy models by learning fuzzy inference operators together with Rule Base. The Multiobjective Evolutionary Algorithm proposed generates a set of Fuzzy Rule Based Systems with diff...

متن کامل

A Mechanism to Improve the Interpretability of Linguistic Fuzzy Systems with Adaptive Defuzzification based on the use of a Multi-objective Evolutionary Algorithm

This paper proposes a mechanism that helps improve the interpretability of linguistic fuzzy ruled based systems with common adaptive defuzzification methods. Adaptive defuzzification significantly improves the system accuracy, but introduces weights associated with each rule of the rule base, decreasing the system interpretability. The suggested mechanism is based on three goals: 1) reducing th...

متن کامل

Adaptive Defuzzification for Fuzzy Systems Modeling

We propose a new parameterized method for the defuzzification process based on the simple M-SLIDE transformation. We develop a computationally efficient algorithm for learning the relevant parameter as well as providing a computationally simple scheme for doing the defuzzification step in the fuzzy logic controllers. The M-SLJDE method results in a particularly simple linear form of the algorit...

متن کامل

A Comparative Study for Identification of Distributed Parameter Systems using Sensor Networks and Adaptive-Network-Based Fuzzy Inference

The paper presents a study upon the possibility to use adaptive-network-based fuzzy inference method (ANFIS) in the identification of distributed parameter systems, implementing a distributed sensor network in the system. Some main properties of different identification methods are presented with possible application. The fuzzy systems, implemented using rule bases, fuzzy values, membership fun...

متن کامل

Evaluation of the Efficiency of the Adaptive Neuro Fuzzy Inference System (ANFIS) in the Modeling of the Ionosphere Total Electron Content Time Series Case Study: Tehran Permanent GPS Station

Global positioning system (GPS) measurements provide accurate and continuous 3-dimensional position, velocity and time data anywhere on or above the surface of the earth, anytime, and in all weather conditions. However, the predominant ranging error source for GPS signals is an ionospheric error. The ionosphere is the region of the atmosphere from about 60 km to more than 1500 km above the eart...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • Int. J. Hybrid Intell. Syst.

دوره 1  شماره 

صفحات  -

تاریخ انتشار 2004