Fundamentals of Type-1 Fuzzy Logic Theory

Author(s):  
Erdal Kayacan ◽  
Mojtaba Ahmadieh Khanesar
Author(s):  
Türkan Erbay Dalkiliç ◽  
Seda Sağirkaya

In regression analysis, the data have different distributions which requires to go beyond the classical analysis during the prediction process. In such cases, the analysis method based on fuzzy logic is preferred as alternative methods. There are couple important steps in the regression analysis based on fuzzy logic. One of them is identification of the clusters that generate the data set, the other is the degree of memberships that are determined the grades of the contributions of the data contained in these clusters. In this study, parameter prediction based on type-2 fuzzy clustering is discussed. Firstly, type-1 fuzzy clustering problem was solved by the fuzzy c-means (FCM) method when the fuzzifier index is equal to two. Then the fuzzifier index m is defined as interval number. The membership degrees to the sets are determined by type-2 fuzzy clustering method. Membership degree obtained as a result of clustering based on type-1 and type-2 fuzzy logic are used as weight and parameter prediction using these membership degrees that determined by the proposed algorithm. Finally, the prediction result of the type-1 and type-2 fuzzy clustering parameter is compared with the error criterion based on the difference between observed values and the predicted values.


2014 ◽  
Vol 82 (1) ◽  
pp. 105-117 ◽  
Author(s):  
Pascual Noradino Montes Dorantes ◽  
Marco Aurelio Jiménez Gómez ◽  
Xavier Cantú Rodriguez ◽  
Gerardo Maximiliano Méndez

Author(s):  
N. Samarinas ◽  
C. Evangelides

Abstract The aim of this paper is to implement the fuzzy logic theory in order to estimate the discharge for open channels, which is a well-known physical problem affected by many factors. The problem can be solved by Manning equation but the parameters present uncertainties as to their true-real values. Especially, the Manning n roughness coefficient, which is an empirically derived coefficient, presents quite high variation for different substrates. With the help of fuzzy logic and utilizing a fuzzy transformation method, it is possible to include the uncertainties of the problem in the calculation process. In this case, it is feasible to estimate the discharge, giving more emphasis on different uncertainty rates of the Manning roughness coefficient, while the rest of the parameters remain with constant or zero uncertainty level. By taking different a-cut levels, it was shown that the methodology gives realistic and reliable results, presenting with great accuracy the variations of the water discharge for trapezoidal open channels. This way, a possible underestimation or overestimation of the actual physical condition is avoided, by helping the engineers and researchers to obtain a more comprehensive view of the real physical conditions, thus making better management plans.


Endeavour ◽  
1996 ◽  
Vol 20 (1) ◽  
pp. 44 ◽  
Author(s):  
Dennis H. Rouvray

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