Fuzzy Logic Treatment of the Laminated Composites Fracture

2016 ◽  
Vol 823 ◽  
pp. 479-484
Author(s):  
Daniela Popescu ◽  
Corina Cernăianu ◽  
Cristian Bratu ◽  
Eugenia Stăncuț
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Factor Of Safety ◽  
Laminated Composites ◽  
Laboratory Data ◽  
Experimental Tests ◽  
Logical Operations ◽  
Natural Means

No one wants a material to fail. If it does happen then we have to know what the causes are and then what is the intensity at which it fails. To do this, we to do many experimental tests. The laboratory data for the laminated composites fractures normally exhibit scatter. This implies an element of uncertainty or vagueness in the results. Fuzzy logic is a natural means of expressing vague categories of information through fuzzy sets and offers means of performing logical operations. In this paper are considered some aspects regarding laminated composites fractures using fuzzy logic methods. The fuzzy logic treatment of the case considered in this work clearly show some risk of failures at lower parameter levels than might be expected using a deterministic treatment in which the vagueness of knowledge is masked and which might not be safeguard against by using a factor of safety value.

Fuzzy logic methods in fatigue and creep

2001 ◽  
Vol 36 (4) ◽  
pp. 411-420 ◽  
Author(s):  
J Harris
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Laboratory Data ◽  
Confidence Limits ◽  
Empirical Correlations ◽  
Uniformly Smooth ◽  
Inspection And Maintenance ◽  
Logic Operations ◽  
Maintenance Schedules ◽  
Natural Means

Fatigue and creep laboratory data for metals normally exhibit scatter, which implies an element of uncertainty or vagueness in the results. Such data are usually treated by empirical correlations or by mathematical models with some theoretical basis. Confidence limits are sometimes given based upon an assumed probability distribution. On a fine scale, fracture mechanics studies consider the mechanism of crack growth, assuming a uniformly smooth continuum. The treatments aim to provide design working stresses and also the reliability basis for the formulation of inspection and maintenance schedules. Further uncertainty in the interpretation of laboratory test data in terms of in-service material characteristics arises from a few other sources, even if the material types are nominally the same. Fuzzy logic is a natural means of expressing vague categories of information by means of fuzzy sets and also provides the means of performing logic operations on them. In this work, consideration is given to the application to some aspects of fatigue and creep. Some examples, including fuzzy boundaries between safe and unsafe states, are given to illustrate the methodology, the conclusions are also initially in the form of fuzzy sets. Compared with other methodologies, richer meaning is found in the results.

Penerapan Logika Samar dalam Peramalan Data Runtun Waktu

2011 ◽  
Vol 3 (2) ◽  
pp. 11-15
Author(s):  
Seng Hansun
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Time Series Analysis ◽  
Fuzzy Sets ◽  
Fuzzy System ◽  
Time Series Data ◽  
Fuzzy Logic System ◽  
Series Data ◽  
Series Analysis ◽  
Logic System

Recently, there are so many soft computing methods been used in time series analysis. One of these methods is fuzzy logic system. In this paper, we will try to implement fuzzy logic system to predict a non-stationary time series data. The data we use here is Mackey-Glass chaotic time series. We also use MATLAB software to predict the time series data, which have been divided into four groups of input-output pairs. These groups then will be used as the input variables of the fuzzy logic system. There are two scenarios been used in this paper, first is by using seven fuzzy sets, and second is by using fifteen fuzzy sets. The result shows that the fuzzy system with fifteen fuzzy sets give a better forecasting result than the fuzzy system with seven fuzzy sets. Index Terms—forecasting, fuzzy logic, Mackey-Glass chaotic, MATLAB, time series analysis

NORMAL FORMS FOR FUZZY LOGIC — AN APPLICATION OF KOLMOGOROV’S THEOREM

1996 ◽  
Vol 04 (04) ◽  
pp. 331-349 ◽  
Author(s):  
VLADIK KREINOVICH ◽  
HUNG T. NGUYEN ◽  
DAVID A. SPRECHER
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Normal Form ◽  
Normal Forms ◽  
Approximation Property ◽  
Universal Approximation ◽  
Approximation Result ◽  
Logical Operations ◽  
Kolmogorov's Theorem

This paper addresses mathematical aspects of fuzzy logic. The main results obtained in this paper are: 1. the introduction of a concept of normal form in fuzzy logic using hedges; 2. using Kolmogorov’s theorem, we prove that all logical operations in fuzzy logic have normal forms; 3. for min-max operators, we obtain an approximation result similar to the universal approximation property of neural networks.

scholarly journals Some New De Morgan Picture Operator Triples in Picture Fuzzy Logic

2018 ◽  
Vol 33 (2) ◽  
pp. 143-164
Author(s):  
Cuong Bui Cong ◽  
Roan Thi Ngan ◽  
Le Ba Long
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Basic Algebra ◽  
Algebra Structure ◽  
Picture Fuzzy Sets

A new concept of picture fuzzy sets (PFS) were introduced in 2013, which are directextensions of the fuzzy sets and the intuitonistic fuzzy sets. Then some operations on PFS withsome properties are considered in [ 9,10 ]. Some basic operators of fuzzy logic as negation, tnorms, t-conorms for picture fuzzy sets firstly are defined and studied in [13,14]. This paper isdevoted to some classes of representable picture fuzzy t-norms and representable picture fuzzyt-conorms on PFS and a basic algebra structure of Picture Fuzzy Logic – De Morgan triples ofpicture operators.

Fuzzy sets and fuzzy logic: Theory and applications

Endeavour ◽  
1996 ◽  
Vol 20 (1) ◽  
pp. 44 ◽  
Author(s):  
Dennis H. Rouvray
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Fuzzy Logic Theory

Fuzzy sets and t-norms in the light of fuzzy logic

1988 ◽  
Vol 29 (2) ◽  
pp. 113-127 ◽  
Author(s):  
Vilém Novák ◽  
Witold Pedrycz
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Sets
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