Fuzzy Rule Base Generation through Genetic Algorithms and Bayesian Classifiers A Comparative Approach

2007 ◽  
Author(s):  
Marcos Evandro Cintra ◽  
Estevan R. Hruschka ◽  
Heloisa de A. Camargo ◽  
M. do Carmo Nicoletti
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Rule ◽  
Comparative Approach ◽  
Rule Base ◽  
Bayesian Classifiers ◽  
Fuzzy Rule Base

An Intelligent Automatic Surveillance System via Fuzzy Rule Base System and Genetic Algorithms

1998 ◽  
Vol 2 (5) ◽  
pp. 151-159
Author(s):  
Hyung-Jin Kang ◽  
◽  
Heejin Lee ◽  
Heung-Sik Noh ◽  
Jung-Hwan Kim ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Surveillance System ◽  
Fuzzy Rule ◽  
Moving Object ◽  
Approximate Reasoning ◽  
Rule Base ◽  
Base System ◽  
Fuzzy Rule Base ◽  
Object Feature ◽  
Object Features

In this paper, an intelligent Automatic Surveillance System is proposed using fuzzy rule base system and genetic algorithms. The aim of our Automatic Surveillance System is to detect a moving object and make a decision on whether it is human or not. Various object features such as the ratio of the width and the length of the moving object, the distance dispersion between the principal axis and the object contour, the eigenvectors, the symmetric axes, and the areas of the segmented regions are used in this paper. These features are not unique and decisive characteristics for representing human. Also, due to the outdoor image property, the object feature information is unavoidably vague and inaccurate. In order to make an efficient decision from that information, we use a fuzzy rule base system as an approximate reasoning method The fuzzy rules, combining various object features, are able to describe the conditions for making an intelligent decision. The fuzzy rule base system is initially constructed by heuristic information and then, trained and tested with input/output data. For the effective training, the well-known Genetic Algorithms (GA) are used. Experimental results are shown, demonstrating the validity of our system.

scholarly journals Intelligent Approach to Inventory Control in Logistics under Uncertainty Conditions

2016 ◽  
Author(s):  
Paweł Więcek
Keyword(s):  
Genetic Algorithms ◽  
Lead Time ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Additional Element ◽  
Fuzzy Rule Base ◽  
Combined Application ◽  
Lead Time Uncertainty ◽  
Intelligent Approach ◽  
The Impact

The article presents a proposal for a combined application of fuzzy logic and genetic algorithms to control the procurement process in the enterprise. The approach presented in this paper draws particular attention to the impact of external random factors in the form of demand and lead time uncertainty. The model uses time-variable membership function parameters in a dynamic fashion to describe the modelled output fuzzy (sets) values. An additional element is the use of genetic algorithms for optimisation of fuzzy rule base in the proposed method. The approach presented in this paper was veryfied according to four criteria based on a computer simulation performed on the basis of the actual data from an enterprise.DOI: http://dx.doi.org/10.4995/CIT2016.2016.3508 

Fuzzy Rule Base Design With Probabilistic Weights

2016 ◽  
Author(s):  
Leonardo G. Melo ◽  
Luís A. Lucas ◽  
Myriam R. Delgado
Keyword(s):  
Fuzzy Rule ◽  
Rule Base ◽  
Fuzzy Rule Base ◽  
Base Design
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