Fuzzy Information Inequalities and Application in Pattern Recognition

2021 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Ram Naresh Saraswat ◽  
Sapna Gahlot
Keyword(s):  
Pattern Recognition ◽  
Fuzzy Information ◽  
Information Inequalities

GENERALIZED FUZZY DIVERGENCE MEASURE, PATTERN RECOGNITION AND INEQUALITIES

2022 ◽  
Vol 11 (1) ◽  
pp. 0-0
Keyword(s):  
Pattern Recognition ◽  
Convex Function ◽  
Convex Functions ◽  
Divergence Measure ◽  
Fuzzy Information ◽  
Information Inequalities ◽  
Fuzzy Concept ◽  
Divergence Measures ◽  
Fuzzy Divergence

Many fuzzy information and divergence measures developed by various researchers and authors. Here, authors proposed new fuzzy divergence measure using the properties of convex function and fuzzy concept. The applications of novel fuzzy divergence measures in pattern recognition with case study, are discussed. Obtained various novel fuzzy information inequalities on fuzzy divergence measures. The new relations among new and existing fuzzy divergence measure by new f-divergence, Jensen inequalities, properties of convex functions and inequalities have studied. Finally, verified these results and proposed fuzzy divergence measures by numerical example.

General Expression Forms on the Fuzziness Degree of Fuzzy Operator

2010 ◽  
Vol 40-41 ◽  
pp. 835-838
Author(s):  
Zhao Jun Liu
Keyword(s):  
Artificial Intelligence ◽  
Pattern Recognition ◽  
Information Processing ◽  
Set Theory ◽  
Fuzzy Set Theory ◽  
Fuzzy Reasoning ◽  
Fuzzy Information ◽  
Fuzzy Operator ◽  
Fuzzy Pattern Recognition ◽  
Fuzzy Pattern

In order to study fuzziness degree's quantity measure as to fuzzy operator, using fuzzy set theory, the problem of general expressions on fuzziness degree as to fuzzy operator has been studied, a series of theorems and formulas about which have been given. These formulas have a special foundation function in fuzzy information processing, such as the fuzzy pattern recognition, the fuzzy reasoning, and the fuzzy artificial intelligence. The great theory supports have been supplied to computer's fuzzy information processing.

AN APPROACH FOR MULTIPLE ATTRIBUTE GROUP DECISION MAKING BASED ON INTUITIONISTIC FUZZY INFORMATION

2009 ◽  
Vol 17 (03) ◽  
pp. 317-332 ◽  
Author(s):  
ZHONGLIANG YUE ◽  
YUYING JIA ◽  
GUODONG YE
Keyword(s):  
Decision Making ◽  
Pattern Recognition ◽  
Group Decision Making ◽  
Fuzzy Number ◽  
Group Decision ◽  
Intuitionistic Fuzzy Set ◽  
Intuitionistic Fuzzy Number ◽  
Fuzzy Information ◽  
Intuitionistic Fuzzy ◽  
Multiple Attribute

Intuitionistic fuzzy set, was introduced by Atanassov, has been applied to many different fields, such as logic programming, pattern recognition, and decision making, etc. However, so far there has been few investigation on how to transform attribute tested values of alternative into a intuitionistic fuzzy number, and then complete decision making by intuitionistic fuzzy information. In this paper, the original attribute value (objective information) are characterized by crisp number which are given by decision maker. We define the concepts of supporting, opposing and neutral set of alternative respectively, develop an approach for transform attribute values into intuitionistic fuzzy number, and determine the order of alternatives based on the score and the degree of accuracy of the intuitionistic fuzzy number. Finally, a practical example is provided to illustrate the developed method.

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

Bayesian removal of noise for increased sensitivity in vector pattern recognition lattice imaging of interfaces

1993 ◽  
Vol 51 ◽  
pp. 994-995
Author(s):  
L. Fei ◽  
P. Fraundorf
Keyword(s):  
Pattern Recognition ◽  
Perfect Crystal ◽  
Unit Cell ◽  
Ion Milling ◽  
Beam Radiation ◽  
Major Interest ◽  
Unit Cells ◽  
Mapping Process ◽  
Increased Sensitivity ◽  
Electron Microscopes

Interface structure is of major interest in microscopy. With high resolution transmission electron microscopes (TEMs) and scanning probe microscopes, it is possible to reveal structure of interfaces in unit cells, in some cases with atomic resolution. A. Ourmazd et al. proposed quantifying such observations by using vector pattern recognition to map chemical composition changes across the interface in TEM images with unit cell resolution. The sensitivity of the mapping process, however, is limited by the repeatability of unit cell images of perfect crystal, and hence by the amount of delocalized noise, e.g. due to ion milling or beam radiation damage. Bayesian removal of noise, based on statistical inference, can be used to reduce the amount of non-periodic noise in images after acquisition. The basic principle of Bayesian phase-model background subtraction, according to our previous study, is that the optimum (rms error minimizing strategy) Fourier phases of the noise can be obtained provided the amplitudes of the noise is given, while the noise amplitude can often be estimated from the image itself.

Pattern Recognition: The Basis of All Cognition?

1989 ◽  
Vol 34 (11) ◽  
pp. 988-989
Author(s):  
Erwin M. Segal
Keyword(s):  
Pattern Recognition
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