- Fuzzy logic was developed by Lotfi Zadeh to address applications involving subjective or vague data like "attractive person" that cannot be easily analyzed using binary logic. It allows for partial truth values between completely true and completely false.
- Fuzzy logic controllers mimic human decision making and involve fuzzifying inputs, applying fuzzy rules, and defuzzifying outputs. This allows systems to be specified in human terms and automated.
- Fuzzy logic has many applications from industrial process control to consumer products like washing machines and microwaves. It offers an intuitive way to model real-world ambiguities compared to mathematical or logic-based approaches.
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
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.
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
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.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
The Fuzzy Logic is discussed with three simple example problems all solved in MATLAB
1. Restaurant Problem
2. Temperature Controller
3. Washing Machine Problem
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.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
The Fuzzy Logic is discussed with three simple example problems all solved in MATLAB
1. Restaurant Problem
2. Temperature Controller
3. Washing Machine Problem
This is an easy introduction to the concept of Genetic Algorithms. It gives Simple explanation of Genetic Algorithms. Covers the major steps that are required to implement the GA for your tasks.
For other resources visit: http://pimpalepatil.googlepages.com/
For more information mail me on pbpimpale@gmail.com
This presentation is intended for giving an introduction to Genetic Algorithm. Using an example, it explains the different concepts used in Genetic Algorithm. If you are new to GA or want to refresh concepts , then it is a good resource for you.
Presentation is about genetic algorithms. Also it includes introduction to soft computing and hard computing. Hope it serves the purpose and be useful for reference.
The SlideShare 101 is a quick start guide if you want to walk through the main features that the platform offers. This will keep getting updated as new features are launched.
The SlideShare 101 replaces the earlier "SlideShare Quick Tour".
I Planned to give a specific training on Fuzzy Logic Controller using MATLAB simulation. This type of intelligent controller is very useful for the research work in all discipline.
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.
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
Theories of induction in psychology and artificial intelligence assume that the process leads from observation and knowledge to the formulation of linguistic conjectures. This paper proposes instead that the process yields mental models of phenomena. It uses this hypothesis to distinguish between deduction, induction, and creative forms of thought. It shows how models could underlie inductions about specific matters. In the domain of linguistic conjectures, there are many possible inductive generalizations of a conjecture. In the domain of models, however, generalization calls for only a single operation: the addition of information to a model. If the information to be added is inconsistent with the model, then it eliminates the model as false: this operation suffices for all generalizations in a Boolean domain. Otherwise, the information that is added may have effects equivalent (a) to the replacement of an existential quantifier by a universal quantifier, or (b) to the promotion of an existential quantifier from inside to outside the scope of a universal quantifier. The latter operation is novel, and does not seem to have been used in any linguistic theory of induction. Finally, the paper describes a set of constraints on human induction, and outlines the evidence in favor of a model theory of induction.
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.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
2. INTRODUCTION
Fuzzy logic has rapidly become one of the most
successful of today's technologies for developing
sophisticated control systems. The reason for which is
very simple.
Fuzzy logic addresses such applications perfectly as it
resembles human decision making with an ability to
generate precise solutions from certain or
approximate information.
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.
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3. While other approaches require accurate equations to
model real-world behaviors, fuzzy design can
accommodate the ambiguities of real-world human
language and logic.
It provides both an intuitive method for describing
systems in human terms and automates the
conversion of those system specifications into
effective models.
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4. CHRONICLE:-
Lotfi A. Zadeh, a professor of UC Berkeley in
California, soon to be known as the founder of fuzzy
logic observed that conventional computer logic was
incapable of manipulating data representing subjective
or vague human ideas such as "an atractive person" .
Fuzzy logic, hence was designed to allow computers
to determine the distinctions among data with shades
of gray, similar to the process of human reasoning.
This theory proposed making the membership
function (or the values False and True) operate over
the range of real numbers [0.0, 1.0]. Fuzzy logic was
now introduced to the world.
04/27/12 4
5. t d o yo u m e a n b y f u z z y
Fuzzy logic is a superset of Boolean logic that has
been extended to handle the concept of partial truth-
truth values between "completely true" and
"completely false".
The essential characteristics of fuzzy logicare as
follows:-
In fuzzy logic, exact reasoning is viewed as a limiting
case of approximate reasoning.
In fuzzy logic everything is a matter of degree.
Any logical system can be fuzzified
In fuzzy logic, knowledge is interpreted as a collection
of elastic or, equivalently , fuzzy constraint on a
collection of variables
The third statement hence, define Boolean logic as a
subset of Fuzzy logic.
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6. F uzzy S e ts
A paradigm is a set of rules and regulations which
defines boundaries and tells us what to do to be
successful in solving problems within these
boundaries.
For example the use of transistors instead of vacuum
tubes is a paradigm shift - likewise the development of
Fuzzy Set Theory from conventional bivalent set
theory is a paradigm shift.
Bivalent Set Theory can be somewhat limiting if we
wish to describe a 'humanistic' problem
mathematically.
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7. Fig. below illustrates bivalent sets to
characterise the temperature of a room.
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8. F u z z y S e t O p e r a t io n s .
U n io n
The membership function of the Union of two fuzzy sets A
and B with membership functions and respectively is
defined as the maximum of the two individual membership
functions. This is called the maximum criterion.
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9. W h a t d o e s it o f f e r ?
The first applications of fuzzy theory were primarily
industrial, such as process control for cement kilns.
Since then, the applications of Fuzzy Logic technology
have virtually exploded, affecting things we use
everyday.
Take for example, the fuzzy washing machine .
A load of clothes in it and press start, and the
machine begins to churn, automatically choosing the
best cycle. The fuzzy microwave, Place chili,
potatoes, or etc in a fuzzy microwave and push single
button, and it cooks for the right time at the proper
temperature.
The fuzzy car, maneuvers itself by following simple
verbal instructions from its driver. It can even stop
itself when there is an obstacle immediately ahead 9
04/27/12
using sensors.
10. H o w d o f u z z y s e t s d if f e r
f r o m c la s s ic a l s e t s ?
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.
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11. E Q U A L IT Y O F F U Z Z Y S E T S :-
Let A={ Mohan/.2;Sohan/1;John/7;Abrahm/4}
B= {Abrahm/4;Mohan/.2;John/7;Sohan/1}
However, if
C={Abrahm/2;Mohan/.4;Sohan/1;John}
A = B and A ≠ C
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12. F U Z Z Y C O N TR O L :-
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:
1)Fuzzification(Using membership functions to
graphically describe a situation)
2)Rule evaluation(Application of fuzzy rules)
3)Defuzzification(Obtaining the crisp or actual results)
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13. WH Y F U Z Z Y C O N TR O L ?
Fuzzy Logic is a technique to embody human like
thinking into a control system.
A fuzzy controller is designed to emulate human
deductive thinking, that is, the process people use to
infer conclusions from what they know.
Traditional control approach requires formal modeling
of the physical reality.
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14. A f u z z y c o n t r o l s y s t e m can also be
described as based on fuzzy logic—a mathematical
system that analyzes analog input values in terms of
logical variables that take on continuous values
between 0 and 1, in contrast to classical or digital
logic, which operates on discrete values of either 1 or
0 (true or false respectively).
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15. Fuzzy logic is widely used in machine control.
The term itself inspires a certain skepticism, sounding
equivalent to "half-baked logic" or "bogus logic", but
the "fuzzy" part does not refer to a lack of rigour in the
method, rather to the fact that the logic involved can
deal with fuzzy concepts—concepts that cannot be
expressed as "true" or "false" but rather as "partially
true".
04/27/12 15
16. Although genetic algorithms and neural networks can
perform just as well as fuzzy logic in many cases,
fuzzy logic has the advantage that the solution to the
problem can be cast in terms that human operators
can understand,
so that their experience can be used in the design of
the controller. This makes it easier to mechanize tasks
that are already successfully performed by humans.
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17. L IT T L E M O R E O N F U Z Z Y
C O N T R O L :-
Fuzzy controllers are very simple conceptually.
They consist of an input stage, a processing
stage, and an output stage.
The input stage maps sensor or other inputs,
such as switches, thumbwheels, and so on, to the
appropriate membership functions and truth
values.
The processing stage invokes each appropriate
rule and generates a result for each, then
combines the results of the rules. Finally, the
output stage converts the combined result back
into a specific control output value.
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18. H o w f a r c a n f u z z y lo g ic
go???
It can appear almost anyplace where computers and
modern control theory are overly precise as well as in
tasks requiring delicate human intuition and
experience-based knowledge. What does the future
hold?
Computers that understand and respond to normal
human language.
Machines that write interesting novels and
screenplays in a selected style , such as
Hemingway's.
Molecule-sized soldiers of health that will roam the
blood-stream, killing cancer cells and slowing the
04/27/12 18
aging process.
19. Hence, it can be seen that with the enormous
research currently being done in Japan and many
other countries whose eyes have opened, the future
of fuzzy logic is undetermined. There is no limit to
where it can go.
The future is bright. The future is fuzzy.
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