One of the principal applications of fuzzy logic is in control system design. Fuzzy logic controllers (FLC) can be used to control
systems where the use of conventional control techniques may be Problematic. The tuning of fuzzy controllers has tende
d to rely on human expert knowledge, but where the number of rules and fuzzy sets is large. The Problem of generation desirable fuzzy rule is very important in the development of fuzzy systems. The purpose of this paper is to present a generation method of fuzzy control rules by learning from examples using genetic algorithms (GA). We propose real coded genetic algorithms (RCGA) for learning fuzzy rules, and an iterative process for obtaining set of rules which covers the examples set with a covering value previously defined.
Environment is the atmosphere where mankind lives and interact
with its component, whereas the growth of human's requirements
has made great changes of the environmental system .So the
environmental evaluation become one of the most important tool
to reduce the impact of enhancement projects .Concrete works impact seems to be complicated and massive but some of them has lately effects which never appear soon. Nevertheless, the environmental evaluation considered very necessary and has such a big importance as a tool to make a decision, so that the concrete equipments are the serious element in environmental evaluation of concrete projects.
Environmental evaluation to Huge concrete works will directly
focus on the concrete equipments which have the clear effects on
environment so the Environmental evaluation purpose is to reduce
or prevent the negative effects which is expected in such as works,
also it can be used as a tool in management and planning projects
through inserting the environmental consideration in projects
planning. In order to discuss this problem we used the fuzzy logic theory.
The overlay functions in Geographic Information Systems (GIS) are considered as one of the basic functions of these systems, and often, a variety of data stored in layers may be integrated together to generate new layers that contain useful informati
on for decision-makers. All geographic objects are stored in layers and usually rely on crisp set theory, whether, stored in a vector or raster format. In many cases, boundaries of classes or objects are not clearly defined, or when we perform classification of features into classes, the geographic objects located in the boundaries of classes could be classified into the wrong class. This research aims to develop overlay functions methodology based on fuzzy logic, reclassify the objects into fuzzy classes, and study the usability of this method to integrate data of specific phenomenon to help make optimal decisions. To implement and examine this idea, a set of Fuzzy Membership Functions was developed using the Python programming language embedded within the ArcGIS environment. Through this Fuzzy Membership Functions, the user can generate fuzzy sets and combine with each other according to one of fuzzy operations, and thus the generation of fuzzy sets allows supporting the right and reliable decision. To test the proposed fuzzy model capabilities, it has been applied in Tartous governorate to select suitable tourist facility sites in accordance with groups of factors. In summary, data integration using proposed fuzzy overlay functions can improve the reliability of data representation and thus the reliability of make the best decisions.
The entry of computer to many areas, such as medical field, led to develop new
technique that has led to the prosperity of these areas, and helped doctors to detect and
diagnose diseases accurately and credibility, where the experience of the docto
r in addition
to the accuracy of computer lead to access to the credibility of high patient and save
human lives.
A new approach for cardiac diseases detection and classification in ECG signals
images is proposed using Adaptive Neuro Fuzzy Inference System ANFIS.
The proposed approach is applied on database containing (147) ECG images,
each of them accompanied with its medical report. The medical reports were used to
validate the detection and classification.
The proposed method achieved a relatively high accuracy (97%) in detection and
classification processes.
The proposed approach is developed using MATLAB, and based on its libraries,
image processing, neural network and fuzzy logic.
The aim of this study is to develop a fuzzy inference model to
estimate the impact of change orders on the duration of
construction projects in Syria. This can help in obtaining the
optimal estimation of the increasing of the project duration caus
ed
by the changes. The capability of gradient estimation of the fuzzy
logic approach reduces the distrust of estimation using factor
assessment. Also it estimates the duration of the project after any
change.via the experts evaluation or according to the crisp logic.
This study has been done to develop scientific research and select talented people of
post-graduate students (master students) to continue and get doctoral degrees (degree in
PHD) at Tishreen University. The research has been prepared, which aims t
o suggest a
model for measuring the degree of creativity and talent for post-graduate students by using
one of artificial intelligence techniques such as Fuzzy Logic.
An expert system has been built that contains an inference rule which consists of
three types of tests: (Theory Test, TT), (Practice Test, PT), and (Creativity Test, CT) for
each course.
This intelligent system has also aimed to determine the ability to make decisions
which gives the rate of talent for post- graduate students.
The study has reached to an important set of results, and the most important is:
Results have shown high strength and reliability shows the validity of this proposed
model, the validity of the results have reached to 85% and 100%, by using two different
methods to defuzzificate of proposed model.
This paper presents the proposed Method for designing fuzzy
supervisory controller model for Proportional Integral Differential
controller (PID) by Fuzzy Reasoning Petri Net (FRPN),the Features
of Method shows the fuzzification value for each prop
erty of
membership function for each input of fuzzy supervisory controller,
and determine the total number of rules required in designing the
controller before enter the appropriate rules in the design phase of
the rules, and determine the value of the inputs of the rule that has
been activated, and assembly variables that have the same property
and show the value for each of them programmatically, and
determine the deffuzification value using deffuzification methods.
The facilities in Syria are destroyed and damaged by terrorist acts
of armed groups. Therefore, the construct managers should
rehabilitate it logically and economically. This required accurate
data about the structural nature and functional condit
ions of the
construct. However, the personal estimation of experts and the
approximate structural conditions and functionality of construct for
rehabilitation- inclines the decision makers from getting the exact
estimate of the rehabilitation degrees. Therefore the aim of this
research is to propose a set of indicators and criteria that can
assist in selecting the construct that required to be rehabilitated
according to its levels.
Suspension system is considered one of the most important components of modern
automobiles as it is the responsible for the vehicle’s stability, balance and safety. The
presence of robust controller is very necessary in order to ensure full interac
tion between
suspension components and making accurate decisions at the right time. This paper
proposes to design an Extended Adaptive Neuro Fuzzy Inference System (EANFIS)
controller for suspension system in quarter car model. The proposed controller is used as
decision maker In order to contribute in absorbing shocks caused by bumpy roads, and to
prevent vibrations from reaching the cockpit. Furthermore, it provides stability and
coherence required to reduce the discomfort felt by passengers, which arises from road
roughness, which in turn, improve the road handling. The MATLAB Simulink is used to
simulate the proposed controller with the controlled model and to display the responses of
the controlled model under different types of disturbance. In addition, a comparison
between EANFIS controller, Fuzzy controller and open loop model (passive suspension)
was done with different types of disturbance on order to evaluate the performance of the
proposed model. Controller has shown excelled performance in terms of reducing
displacements, velocity and acceleration.
This research
aims to develop overlay functions methodology based on fuzzy
logic, reclassify the objects into fuzzy classes, and study the
usability of this method to integrate data of specific phenomenon
to help make optimal decisions.
