Fuzzy Logic
Where did it begin?
What is Fuzzy Logic?
Fuzzy Logic in Control Systems
Fuzzy Logic in Other Fields
Fuzzy Logic vs. Neural Networks
Fuzzy Logic Benefits
How can you deal with Fuzzy Logic. Fuzzy logic is a form of many-valued logic; it deals with reasoning that is approximate rather than fixed and exact. In contrast with traditional logic theory, where binary sets have two-valued logic: true or false, fuzzy logic variables may have a truth value that ranges in degree
between 0 and 1
This presentation includes what is fuzzy logic, characteristics, membership function with example, fuzzy set theory, De-Morgans Law, Fuzzy logic V/S probability, advantages and disadvantages and application areas of fuzzy logic. This is a presentation is useful for IT students.
Its ability to deal with vague systems and its use of linguistic variables. Leads to faster and simpler program development of system controllers. It can be a decision support system tool for managers.
How can you deal with Fuzzy Logic. Fuzzy logic is a form of many-valued logic; it deals with reasoning that is approximate rather than fixed and exact. In contrast with traditional logic theory, where binary sets have two-valued logic: true or false, fuzzy logic variables may have a truth value that ranges in degree
between 0 and 1
This presentation includes what is fuzzy logic, characteristics, membership function with example, fuzzy set theory, De-Morgans Law, Fuzzy logic V/S probability, advantages and disadvantages and application areas of fuzzy logic. This is a presentation is useful for IT students.
Its ability to deal with vague systems and its use of linguistic variables. Leads to faster and simpler program development of system controllers. It can be a decision support system tool for managers.
Fuzzy logic is often heralded as a technique for handling problems with large amounts of vagueness or uncertainty. Since its inception in 1965 it has grown from an obscure mathematical idea to a technique used in a wide variety of applications from cooking rice to controlling diesel engines on an ocean liner.
This talk will give a layman's introduction to the topic and explore some of the real world applications in control and human decision making. Examples might include household appliances, control of large industrial plant, and health monitoring systems for the elderly. We will look at where the field might be going over the next ten years, highlighting areas where DMU's specialist expertise drives the way.
Defuzzification is the process of producing a quantifiable result in Crisp logic, given fuzzy sets and corresponding membership degrees. It is the process that maps a fuzzy set to a crisp set. It is typically needed in fuzzy control systems.
Fuzzy logic is often heralded as a technique for handling problems with large amounts of vagueness or uncertainty. Since its inception in 1965 it has grown from an obscure mathematical idea to a technique used in a wide variety of applications from cooking rice to controlling diesel engines on an ocean liner.
This talk will give a layman's introduction to the topic and explore some of the real world applications in control and human decision making. Examples might include household appliances, control of large industrial plant, and health monitoring systems for the elderly. We will look at where the field might be going over the next ten years, highlighting areas where DMU's specialist expertise drives the way.
Defuzzification is the process of producing a quantifiable result in Crisp logic, given fuzzy sets and corresponding membership degrees. It is the process that maps a fuzzy set to a crisp set. It is typically needed in fuzzy control systems.
Performance analysis of various parameters by comparison of conventional pitc...eSAT Journals
Abstract This paper deals with a variable speed wind turbine coupled with a permanent magnet synchronous generator connected through a two mass drive train. This drive train is connected to synchronous generator and after the conversion process finally connected to grid and the idea of transmission over a long distance makes the use of converter necessary and at the receiving end. The inverter is used to convert it back and the inverter is designed with a proper gate signal to get the best output three phase voltages. The fuzzy logic controller is used to track generator speed with varying wind speed to optimize turbine aerodynamic efficiency in the outer speed loop. Pitch angle control of wind turbine has been used widely to reduce torque and output power variation in high rated wind speed areas .The machine side converter is designed to extract maximum power from the wind. In this work a WECS connected with grid is designed in Matlab and a Fuzzy controller is designed to improve the output and we can see the major difference in DC link voltage and reactive power in transmission line. From the outputs we can also go through the reactive power issue which system is best for inductive load or capacitive load. The simple PI system is good for capacitive load and the fuzzy system is better option for the inductive load. The results of both the system of normal controller and fuzzy controller is compared and analyzed. Key Words: Fuzzy logic controller (FLC), permanent magnet synchronous generator (PMSG), insulated gate bipolar transistor (IGBT) , Pulse width modulation (PWM), Wind energy conversion system, DC link capacitor. FACTS Flexible A.C Transmission system, PI proportional integral
The aim of this paper is to prove that fuzzy logic algorithm is a suitable control technique for fast processes such as electrical machines. This theory has been experimented on different kinds of electrical machines such as stepping motors, dc motors and induction machines (with 6 phases) and the experimental results show that the proposed fuzzy logic algorithm is the most suitable control technique for electrical machines since this algorithm is not time consuming and it is also robust between plant parameters variations.
Speed Control of Brushless Dc Motor Using Fuzzy Logic Controlleriosrjce
This paper presents a control scheme of a fuzzy logic for the brushless direct current (BLDC)
permanent magnet motor drives. The mathematical model of BLDC motor and fuzzy logic algorithm is derived.
The controller is designed to tracks variations of speed references and stabilizes the output speed during load
variations. The BLDC has some advantages compare to the others type of motors, however the nonlinearity of
the BLDC motor drive characteristics, because it is difficult to handle by using conventional proportionalintegral
(PI) controller. The BLDC motor is fed from the inverter where the rotor position and current
controller is the input. In order to overcome this main problem, the fuzzy logic control is learned continuously
and gradually becomes the main effective control. The effectiveness of the proposed method is verified by
develop simulation model in MATLAB-Simulink program. The simulation results show that the proposed fuzzy
logic controller (FLC) produce significant improvement control performance compare to the PI controller for
both condition controlling speed reference variations and load disturbance variations. Fuzzy logic is introduced
in order to suppressing the chattering and enhancing the robustness of the controlled system. Fuzzy boundary
layer is developed to provide smother transition to the equivalent control. Smaller overshoot in the speed
response and much better disturbance rejecting capabilities.
Slides used in the Chapter 2 (Genetic Fuzzy Systems) of the Seminar (Artificial Intelligence Techniques at Engineering). 4th part: Intelligent Transportation Systems
Logika Fuzzy merupakan suatu logika yang memiliki nilai kekaburan atau kesamaran (fuzzyness) antara benar atau salah. Dalam logika klasik dinyatakan bahwa segala hal dapat
diekspresikan dalam istilah binary (0 atau 1, hitam atau putih, ya atau tidak), sedangkan logika fuzzy memungkinkan nilai keanggotaan antara 0 dan 1, tingkat keabuan dan juga hitam dan putih, dan dalam bentuk linguistik, konsep tidak pasti seperti "sedikit", "lumayan" dan "sangat". Logika ini berhubungan dengan himpunan fuzzy dan teori kemungkinan. Logika fuzzy ini diperkenalkan oleh Dr. Lotfi Zadeh dari Universitas California, Berkeley pada 1965. Logika fuzzy dapat digunakan dalam bidang teori kontrol, teori keputusan, dan beberapa bagian dalam managemen sains. Selain itu, kelebihan dari logika fuzzy adalah kemampuan dalam proses penalaran secara bahasa (linguistic reasoning), sehingga dalam perancangannya tidak memerlukan persamaan matematik dari objek yang dikendalikan.
Determination of the preceptor is one of academic obligations. Undesirable things always happen in getting optimal decisions in which faculty are assigned not the most appropriate to the topic of thesis. This matter can affect the result and the quality of the thesis. The research process uses the input variable of lecturers criteria. The data will be processed by using the method of fuzzy logic to obtain the output consists of preceptors and examiners. In this case, the students do not have to worry about the competence of the lectures since the lecturers who have been given to them are fully filtered.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Online aptitude test management system project report.pdfKamal Acharya
The purpose of on-line aptitude test system is to take online test in an efficient manner and no time wasting for checking the paper. The main objective of on-line aptitude test system is to efficiently evaluate the candidate thoroughly through a fully automated system that not only saves lot of time but also gives fast results. For students they give papers according to their convenience and time and there is no need of using extra thing like paper, pen etc. This can be used in educational institutions as well as in corporate world. Can be used anywhere any time as it is a web based application (user Location doesn’t matter). No restriction that examiner has to be present when the candidate takes the test.
Every time when lecturers/professors need to conduct examinations they have to sit down think about the questions and then create a whole new set of questions for each and every exam. In some cases the professor may want to give an open book online exam that is the student can take the exam any time anywhere, but the student might have to answer the questions in a limited time period. The professor may want to change the sequence of questions for every student. The problem that a student has is whenever a date for the exam is declared the student has to take it and there is no way he can take it at some other time. This project will create an interface for the examiner to create and store questions in a repository. It will also create an interface for the student to take examinations at his convenience and the questions and/or exams may be timed. Thereby creating an application which can be used by examiners and examinee’s simultaneously.
Examination System is very useful for Teachers/Professors. As in the teaching profession, you are responsible for writing question papers. In the conventional method, you write the question paper on paper, keep question papers separate from answers and all this information you have to keep in a locker to avoid unauthorized access. Using the Examination System you can create a question paper and everything will be written to a single exam file in encrypted format. You can set the General and Administrator password to avoid unauthorized access to your question paper. Every time you start the examination, the program shuffles all the questions and selects them randomly from the database, which reduces the chances of memorizing the questions.
Water billing management system project report.pdfKamal Acharya
Our project entitled “Water Billing Management System” aims is to generate Water bill with all the charges and penalty. Manual system that is employed is extremely laborious and quite inadequate. It only makes the process more difficult and hard.
The aim of our project is to develop a system that is meant to partially computerize the work performed in the Water Board like generating monthly Water bill, record of consuming unit of water, store record of the customer and previous unpaid record.
We used HTML/PHP as front end and MYSQL as back end for developing our project. HTML is primarily a visual design environment. We can create a android application by designing the form and that make up the user interface. Adding android application code to the form and the objects such as buttons and text boxes on them and adding any required support code in additional modular.
MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software. It is a stable ,reliable and the powerful solution with the advanced features and advantages which are as follows: Data Security.MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software.
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Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
2. OVERVIEW
Where did it begin?
What is Fuzzy Logic?
Fuzzy Logic in Control Systems
Fuzzy Logic in Other Fields
Fuzzy Logic vs. Neural Networks
Fuzzy Logic Benefits
3. Fuzzy Logic began
Traces back to Ancient Greece
Lotfi Asker Zadeh ( 1965 )
First to publish ideas of fuzzy logic.
Professor Toshire Terano ( 1972 )
Organized the world's first working group on fuzzy
systems.
F.L. Smidth & Co. ( 1980 )
First to market fuzzy expert systems.
4. WHAT IS FUZZY LOGIC?
Definition of fuzzy
Fuzzy – “not clear, distinct, or precise; blurred”
Definition of fuzzy logic
A form of knowledge representation suitable for notions that cannot
be defined precisely, but which depend upon their contexts.
5. What is Fuzzy Logic? Contd.
Fuzzy logic is a form of many-valued logic
In contrast with traditional logic theory, where binary sets
have two-valued logic:
true or false,
completely true or completely false
0 or 1
6. WHY FUZZY LOGIC
The reason for the most successful of today's technologies which is very
simple.
Fuzzy logic addresses such applications perfectly as it resembles human
decision making
It fills an important gap in engineering design methods left vacant by
purely mathematical approaches (e.g. linear control design), and
purely logic-based approaches (e.g. expert systems) in system
design.
7. WHY FUZZY CONTROL?
The reasoning in fuzzy logic is similar to human reasoning
It allows for approximate values and inferences as well as
incomplete or ambiguous data (binary yes/no choices)
Fuzzy logic is able to process incomplete data and provide
approximate solutions to problems
8. Fuzzy Control Procedure
Fuzzy control, which directly uses fuzzy rules is the most
important application in fuzzy theory.
Using a procedure originated by Ebrahim Mamdani in the late
70s, three steps are taken to create a fuzzy controlled machine:
Fuzzification(Using membership functions to graphically describe a
situation)
Rule evaluation(Application of fuzzy rules)
DE-fuzzification(Obtaining the crisp or actual results)
9. Degrees of Truth
Both degrees of truth and probabilities range between 0 and 1
and hence may seem similar at first. For example, let a
100 ml glass contain 30 ml of water. Then we may consider
two concepts: Empty and Full. The meaning of each of them
can be represented by a certain fuzzy set.
Then one might define the glass as being 0.7 empty and 0.3
full
10. Applying the Values
In this image, the meaning of the expressions cold, warm, and hot is represented by functions mapping
a temperature scale. A point on that scale has three "truth values"—one for each of the three functions.
12. Traditional Representation of Logic
Slow Fast
Speed = 0 Speed = 1
bool speed;
get the speed
if ( speed == 0) {
// speed is slow
}
else {
// speed is fast
}
13. Fuzzy Logic Representation
For every problem must represent in terms of fuzzy sets
What are fuzzy sets?
Slowest Fastes
t
Slow Fast
[ 0.0 – 0.25 ] [ 0.25 – 0.50
]
[ 0.50 – 0.75
]
[ 0.75 – 1.00
]
14. Fuzzy Logic Representation Cont.
Slowest Fastest
float speed;
get the speed
if ((speed >= 0.0)&&(speed < 0.25)) {
// speed is slowest
}
else if ((speed >= 0.25)&&(speed <
0.5))
{
// speed is slow
}
Slow Fast
else if ((speed >= 0.5)&&(speed < 0.75))
{
// speed is fast
}
else // speed >= 0.75 && speed < 1.0
{
// speed is fastest
}
15. How do fuzzy sets differ from classical
sets?
In classical set theory we assume that all sets rare well-defined (or
crisp), that is given any object in our universe we can always say that
object either is or is not the member of a particular set.
CLASSICAL SETS
The set of people that can run a mile in 4 minutes or less.
The set of children under age seven that weigh more than 1oo pounds.
FUZZY SETS
The set of fast runners
The set of overweight children
16. Applications
ABS Brakes
Expert Systems
Control Units
Bullet train between Tokyo and Osaka
Video Cameras
Automatic Transmissions
Washing Machines
18. TEMPERATURE CONTROLLER
A temperature control system has four settings
Cold, Cool, Warm, and Hot
Change the speed of a heater fan, based off the room temperature and humidity.
19. Anti Lock Break System ( ABS )
Inputs for Intel Fuzzy ABS are derived from
Brake
4 WD
Feedback
Wheel speed
Ignition
Outputs
Pulsewidth
Error lamp
22. Suggested Fuzzy Inference System
Want to Order
Pizza
Recognize
Shop
Service Time
Tip LevelFood Quality
Ambiance
Output Fuzzy MF
for each Phoneme
Assign a Fuzzy Value for
each Phoneme, Output
Highest N Values to a
Linguistic model
24. FUZZY LOGIC VS NEURAL
NETWORKS
How does a Neural Network work?
Both model the human brain.
Fuzzy Logic
Neural Networks
Both used to create behavioural
systems.