Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userGenerating Of Fuzzy Rule from Examples for Fuzzy Control System by Using Genetic Algorithms Real Code
توليد القواعد الضبابية من الأمثلة لنظام تحكم ضبابي باستخدام الخوارزميات الوراثية المشفرة حقيقياً
3725
6 304
0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
إن أحد التطبيقات الرئيسية للمنطق الضبابي هو تصميم نظام تحكم، إن متحكمات المنطق الضبابي (Fuzzy logic controllers (FLC يمكن أن تستعمل لتصميم أنظمة التحكم حيث انه من الصعوبة استخدام تقنيات التحكم التقليدية. لقد اعتمد ضبط المتحكمات الضبابية على المعرفة البشرية الخبيرة و لكن لكون عدد القواعد و المجموعات الضبابية كبير, فان مشكلة إيجاد القواعد الضبابية المرغوبة مهمة جداً في تطوير الأنظمة الضبابية.إن الغرض من هذه المقالة هو تقديم طريقة لتوليد قواعد ضبابية من الأمثلة باستخدام الخوارزميات الوراثية (genetic algorithms (GA و من أجل ذلك نقترح الخوارزميات الوراثية المشفرة حقيقيا (real coded genetic algorithms (RCGA لإيجاد تلك القواعد مع عملية تكرارية للحصول على مجموعة القواعد التي تغطي مجموعة الأمثلة مع قيمة غطاء معرفة بشكل مسبق.
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 tended 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.
References used
Genetic Algorithms; theory and application Ulrich Bodenhofer 2001-2002
Goldberg. D. E. (1989), Genetic Algorithms in Search, Optimization, and Machine Learning, Reading, MA
eds.), Parallel Problem Solving from Nature 2. North-Holland. Baker, J. E. (1995), \Adaptive Selection Methods for Genetic Algorithms," in J. J. Grefenstette (ed.), Proceedings of the First International Conference on Genetic Algorithm )
Hinterding, Robert, Gielewski, Harry and Peachey, T.C. Proceedings of the 3rd International Conference on Genetic Algorithms, 1989. “The Nature of Mutation in Genetic Algorithms” Proceedings of the 6th International on Genetic Algorithms, 1995
George J.Klir /Bo Yuan “Fuzzy Sets and Fuzzy Logic” Theory and Applications, pp 112-140. 2009
rate research
Read More
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.
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.
An ANFIS controller also designed and a comparison
between proposed controller, ANFIS controller and open loop model
had made with different types of disturbance.
The purpose of this article is to shed light on the mechanism
and the procedures of a neuro-fuzzy controller that classifies an
input face into any of the four facial expressions, which are
Happiness, Sadness, Anger and Fear. This program works
a
ccording to the facial characteristic points-FCP which is taken
from one side of the face, and depends, in contrast with some
traditional studies which rely on the whole face, on three
components: Eyebrows, Eyes and Mouth.
As we enter the age of artificial intelligence, the need for intelligent home appliances has become very important for what this smart equipment can provide in the provision of electrical energy and water resources that are treasures should human pre
servation, in addition to the contribution of this equipment to protect the environment from pollution, where we face the challenges next:
High prices of electrical equipment.
The number of hours of electricity supply in many areas is low
because of the current conditions in our country.
- Water shortage.
- The rise in prices of materials used in daily life in general and
household detergents in particular
- Great waste of electricity.
- Pollution of the environment and groundwater with detergents used in the laundry process.
Moreover, the unjust economic blockade imposed on our country is pushing us to work to produce low-cost national housing equipment that competes with foreign products in order to alleviate the material burden on the citizens and promote the national economy.
In order to accomplish this smart washing machine, we have written a code for f type-2 fuzzy microcontroller, using the Python programming language. This controller has received four entries, which are: The first income (clothing color), obtained by taking a picture of the clothes that we need to wash by a camera with a resolution of 8 megapixels, analyzed using OpenCV library, and the second income (clothing type), determined by the local binary pattern algorithm, which is common digital image processing algorithm that widely used to identify shapes that follow specific pattern and structure, the third income (degree of dirt), and was identified by taking a picture of the clothes after soaking them with water for two minutes. The image was then analyzed by the OpenCV library and the fourth (washing weight) that getting From the Load Cell, which measures the physical weights. The readings were converted to digital values via the HX711 digital analogue converter and then sent to Arduino UNO to determine the weight. The weight values were eventually sent to the Raspberry PI for use in the controller. The system generates three exits: washing time (the length of time the laundry was washed), the temperature required for washing, and the amount of detergent required.
After selecting all the previous values, we transferred to control Wattar washing machine model 402, where the water valve was controlled to allow the water to pass into the powder box and from it to the washing basin. The water heater was controlled, which heated the water to the temperature determined by the Fuzzy algorithm, The temperature was monitored by the DS18B20 temperature sensor, which gives a signal to the Raspberry PI at the arrival of the temperature to the required value, and the washing machine engine is controlled for a third of the time specified in the Fuzzy algorithm and we controlled the pump Water to empty basin Washing from water, the process repeated for three consecutive times, we control using a software interface designed using TKinter library
We have been able to design a smart Fuzzy logic type-2 controller with the following advantages:
o save electricity consumption o Provide quantity of detergents o Shortenwashingtime
We have been able to control the following physical components within the washing machine:
o Control the water pump
o control Water valve
o controlMotor
o controlTemperaturesensor o controlLCDscreen
We have built a smart washing machine with the following characteristics:
o Have the ability to recognize the condition of clothes o Identify the type of clothing
o Identify the color of clothes
o Dothewashingwithoutusingapredefinedprogram.
The controller we designed gives good results to calculate the following:
o Washingtime
o Quantity of detergents o Temperature
All diagrams appear in the case of the incremental gradient with an increased degree of dirt and as values correspond to each type of clothing.
Keywords: smart washing machine, saving electricity, saving detergent, shortening washing time, color and clothing distinction, artificial intelligence, fuzzy logic type-2, Raspberry PI, control, Python programming language, HX711.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
