scholarly journals Fingerprints Identification using a Fuzzy Logic System

2010 ◽  
Vol 5 (4) ◽  
pp. 525 ◽  
Author(s):  
Ion Iancu ◽  
Nicolae Constantinescu ◽  
Mihaela Colhon
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Reasoning ◽  
Fuzzy Logic System ◽  
Reasoning System ◽  
Optimal Set ◽  
Logic System

This paper presents an optimized method to reduce the points number to be used in order to identify a person using fuzzy fingerprints. Two fingerprints are similar if n out of N points from the skin are identical. We discuss the criteria used for choosing these points. We also describe the properties of fuzzy logic and the classical methods applied on fingerprints. Our method compares two matching sets and selects the optimal set from these, using a fuzzy reasoning system. The advantage of our method with respect to the classical existing methods consists in a smaller number of calculations.

scholarly journals An Optimization Method Using Clustering Technique for the Human Emotions Detection Artificial Neuro-Fuzzy Logic System

2016 ◽  
Vol 15 (9) ◽  
pp. 7090-7096
Author(s):  
Omayya Murad ◽  
Mohammed Malkawi
Keyword(s):  
Blood Pressure ◽  
Fuzzy Logic ◽  
Optimization Method ◽  
Fuzzy Logic System ◽  
Clustering Method ◽  
Physiological Factors ◽  
Neuro Fuzzy ◽  
Human Emotions ◽  
Optimal Set ◽  
Logic System

This paper utilizes clustering tool in MATLAB to find an optimal set of input parameters for the detection of human emotions using a neuro-fuzzy logic system. Previous studies have relied on a total of 14 physiological factors to detect one or more of 22 different human emotions. In this paper, we use clustering techniques to rank the factors in terms of their significance and impact on the system, and thus find a smaller subset of the factors for the detection of emotions. The clustering method shows that the stroke volume factor (SV) has the lowest impact in the model and as such can be eliminated from the set of factors. The electroencephalography (EEG), heart rate (HR), systolic blood pressure (SBP) and diastolic blood pressure (DBP) are shown to have the highest impact on the model, and must be include in the input set of the model. We compare the clustering method with exhaustive methods for finding the optimal set of factors.

A fuzzy logic system for expert systems

1988 ◽  
Vol 2 (3) ◽  
pp. 183-193 ◽  
Author(s):  
Te-Chuan Chang ◽  
C. William Ibbs ◽  
Keith C. Crandall
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Knowledge Base ◽  
Fuzzy Logic System ◽  
Future Research ◽  
Rule Based ◽  
Reasoning System ◽  
Fuzzy Production Rules ◽  
Future Research Directions ◽  
Logic System

Using the theory of fuzzy sets, this paper develops a fuzzy logic reasoning system as an augmentation to a rule-based expert system to deal with fuzzy information. First, fuzzy set theorems and fuzzy logic principles are briefly reviewed and organized to form a basis for the proposed fuzzy logic system. These theorems and principles are then extended for reasoning based on knowledge base with fuzzy production rules. When an expert system is augmented with the fuzzy logic system, the inference capability of the expert system is greatly expanded; and the establishment of a rule-based knowledge base becomes much easier and more economical. Interpretations of the system’s power and possible future research directions conclude the paper.

APPLICATION OF RULE-BASED FUZZY LOGIC FOR MAIN SUBURBAN ROAD EXTRACTION IN IKONOS IMAGERY

2006 ◽  
Vol 03 (03) ◽  
pp. 171-180
Author(s):  
LILI YUN ◽  
KEIICHI UCHIMURA ◽  
ZHENCHENG HU
Keyword(s):  
Fuzzy Logic ◽  
Satellite Imagery ◽  
Fuzzy Reasoning ◽  
Fuzzy Logic System ◽  
Road Extraction ◽  
Rule Based ◽  
The Road ◽  
Straight Line ◽  
Road Segments ◽  
Logic System

In aerial and satellite imagery, geometric and radiometric properties are two important properties for feature extraction and recognition. This paper presents a semi-automated approach, based on a fuzzy logic system, to extract the main suburban roads in IKONOS images, and shows how to implement structure extraction algorithms based on fuzzy reasoning approaches. First, the method detects segments that are similar to the road in their radiometric properties. Then, it recognizes potential geometric shapes of the road using the straight-line Hough transform. Only the road segments are extracted by means of fuzzy logic concepts, with subsequent image processing and analysis being able to exploit the corresponding fuzzy reasoning to yield improved results. The proposed approach is validated by analysis of high-resolution Ortho-satellite imagery.

A New Hardware Architecture for Fuzzy Logic System Acceleration

2016 ◽  
Vol 12 (2) ◽  
pp. 188-197
Author(s):  
A yahoo.com ◽  
Aumalhuda Gani Abood aumalhuda ◽  
A comp ◽  
Dr. Mohammed A. Jodha ◽  
Dr. Majid A. Alwan
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic System ◽  
Hardware Architecture ◽  
Logic System

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

IMPROVING EIGENSPACE-BASED FUZZY LOGIC SYSTEM USING A LINEAR INTERPOLATION SCHEME FOR SPEECH PATTERN RECOGNITION

2013 ◽  
Vol 37 (3) ◽  
pp. 611-620
Author(s):  
Ing-Jr Ding ◽  
Chih-Ta Yen
Keyword(s):  
Pattern Recognition ◽  
Fuzzy Logic ◽  
Recognition Performance ◽  
Linear Interpolation ◽  
Fuzzy Logic System ◽  
Interpolation Scheme ◽  
Speech Pattern ◽  
Eigen Value ◽  
Target Speaker ◽  
Logic System

The Eigen-FLS approach using an eigenspace-based scheme for fast fuzzy logic system (FLS) establishments has been attempted successfully in speech pattern recognition. However, speech pattern recognition by Eigen-FLS will still encounter a dissatisfactory recognition performance when the collected data for eigen value calculations of the FLS eigenspace is scarce. To tackle this issue, this paper proposes two improved-versioned Eigen-FLS methods, incremental MLED Eigen-FLS and EigenMLLR-like Eigen-FLS, both of which use a linear interpolation scheme for properly adjusting the target speaker’s Eigen-FLS model derived from an FLS eigenspace. Developed incremental MLED Eigen-FLS and EigenMLLR-like Eigen-FLS are superior to conventional Eigen-FLS especially in the situation of insufficient data from the target speaker.

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