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.

ECG Diagnostics by Fuzzy Decision Making

1998 ◽  
Vol 06 (02) ◽  
pp. 201-210 ◽  
Author(s):  
Adolf Grauel ◽  
Lars A. Ludwig ◽  
Georg Klene
Keyword(s):  
Fuzzy Logic ◽  
Medical Knowledge ◽  
Practical Experience ◽  
Fuzzy Logic System ◽  
Fuzzy Decision Making ◽  
Medical Expert ◽  
Rule Based ◽  
Rule Based System ◽  
Cardiac Diagnosis ◽  
Logic System

The analysis of electrocardiograms (ECGs) helps physicians make their cardiac diagnosis. Therefore a large store of medical knowledge and practical experience is required. In this paper we report on our investigations of a rule-based fuzzy logic system that processes ECG data using the knowledge of a medical expert. The aim is to give support to the physician for his diagnosis. In this first consideration we discuss single modules of the rule-based system proposed and moreover we present the used input and output variables of the rulebases. The performance of the implemented rule-based fuzzy logic system is tested using ECGs with abnormalities in the P and T wave as well as in the QRS complex. The system's output corresponds to the analysis of these ECGs by a medical expert.

An Efficient System for Heart Disease Prediction Using Hybrid OFBAT with Rule-Based Fuzzy Logic Model

2016 ◽  
Vol 26 (04) ◽  
pp. 1750061 ◽  
Author(s):  
G. Thippa Reddy ◽  
Neelu Khare
Keyword(s):  
Fuzzy Logic ◽  
Heart Disease ◽  
Fuzzy System ◽  
Disease Diagnosis ◽  
Fuzzy Logic System ◽  
Classification Model ◽  
Rule Based ◽  
Diagnosis System ◽  
Heart Disease Diagnosis ◽  
Logic System

The objective of the work is to predict heart disease using computing techniques like an oppositional firefly with BAT and rule-based fuzzy logic (RBFL). The system would help the doctors to automate heart disease diagnosis and to enhance the medical care. In this paper, a hybrid OFBAT-RBFL heart disease diagnosis system is designed. Here, at first, the relevant features are selected from the dataset using locality preserving projection (LPP) algorithm which helps the diagnosis system to develop a classification model using the fuzzy logic system. After that, the rules for the fuzzy system are created from the sample data. Among the entire rules, the important and relevant group of rules are selected using OFBAT algorithm. Here, the opposition based learning (OBL) is hybrid to the firefly with BAT algorithm to improve the performance of the FAT algorithm while optimizing the rules of the fuzzy logic system. Next, the fuzzy system is designed with the help of designed fuzzy rules and membership functions so that classification can be carried out within the fuzzy system designed. At last, the experimentation is performed by means of publicly available UCI datasets, i.e., Cleveland, Hungarian and Switzerland datasets. The experimentation result proves that the RBFL prediction algorithm outperformed the existing approach by attaining the accuracy of 78%.

scholarly journals Characterization of Human Gait using Fuzzy Logic

10.14311/1789 ◽  
2013 ◽  
Vol 53 (2) ◽  
Author(s):  
Patrik Kutilek ◽  
Slavka Viteckova ◽  
Zdenek Svoboda
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Inclination Angle ◽  
Walking Speed ◽  
Fuzzy Logic System ◽  
Human Walking ◽  
Human Gait ◽  
Fuzzy Expert System ◽  
Movement Type ◽  
Logic System

In medical practice, there is no appropriate widely-used application of a system based on fuzzy logic for identifying the lower limb movement type or type of walking. The object of our study was to determine characteristics of the cyclogram to identify the gait behavior by using a fuzzy logic system. The set of data for setting and testing the fuzzy logic system was measured on 10 volunteers recruited from healthy students of the Czech Technical University in Prague. The human walking speed was defined by the treadmill speed, and the inclination angle of the surface was defined by the treadmill and terrain slope. The input to the fuzzy expert system is based on the following variables: the area and the inclination angle of the cyclogram. The output variables from the fuzzy expert system are: the inclination angle of the surface, and the walking speed. We also tested the method with input based on the angle of inclination of the surface and the walking speed, and with the output based on the area and the inclination angle of the cyclogram. We found that identifying the type of terrain and walking speed on the basis of an evaluation of the cyclogram could be sufficiently accurate and suitable if we need to know the approximate type of walking and the approximate inclination angle of the surface. According to the method described here, the cyclograms could provide information about human walking, and we can infer the walking speed and the angle of inclination of the terrain.

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.

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.

scholarly journals Evolutionary fuzzy logic system for intelligent fibre optic components assembly

2002 ◽  
Vol 216 (5) ◽  
pp. 571-581 ◽  
Author(s):  
D T Pham ◽  
M Castellani
Keyword(s):  
Fuzzy Logic ◽  
Knowledge Base ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Light Output ◽  
Control Policy ◽  
Fuzzy Logic System ◽  
Gradient Based ◽  
Control Knowledge ◽  
Logic System

This paper presents a new evolutionary fuzzy logic system for use in the assembly of optical fibre components. The system optimizes the light output from a fibre by applying a gradient-based algorithm enhanced with momentum information. The parameters of the algorithm are adjusted on-line by a fuzzy controller according to the progress of the alignment process. The control knowledge base is automatically generated via a new evolutionary algorithm. The algorithm divides the population into three subgroups, each concerned with a different level of knowledge-base optimization, and employs a new adaptive selection routine that aims to keep the selection pressure constant throughout the learning phase. The resulting fuzzy logic controller demonstrated a robust performance with alignment times and accuracies comparing favourably against those obtained using a manually designed controller. Moreover, the evolved knowledge base was expressed in a transparent format that facilitated the understanding of the control policy.

Rule-Based Fuzzy Logic System for Diagnosing Migraine

2008 ◽  
pp. 383-388 ◽  
Author(s):  
Svetlana Simić ◽  
Dragan Simić ◽  
Petar Slankamenac ◽  
Milana Simić-Ivkov
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic System ◽  
Rule Based ◽  
Logic System

scholarly journals Selection of a course of action by obstacle employment group based on a fuzzy logic system

2016 ◽  
Vol 26 (1) ◽  
pp. 75-90 ◽  
Author(s):  
Dragan Pamucar ◽  
Darko Bozanic ◽  
Aleksandar Milic
Keyword(s):  
Fuzzy Logic ◽  
Knowledge Base ◽  
Fuzzy Logic System ◽  
Decision Makers ◽  
Rule Base ◽  
Defining Moment ◽  
Course Of Action ◽  
Selection Of ◽  
Logic System

The paper has focused on the point at which a decision is to be made to activate the Obstacle Employment Group (OEG), which is a defining moment for the overall engagement of the group. The course of action is selected based on a Fuzzy Logic System (FLS), created by translating the experience of decision-makers into a single knowledge base. The FLS mainstays are four input criteria and one output, interconnected by a rule base.

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