This document provides an overview of fuzzy logic. It begins by defining fuzzy as not being clear or precise, unlike classical sets which have clear boundaries. It then explains fuzzy logic allows for partial set membership rather than binary membership. The document outlines fuzzy logic's ability to model imprecise or nonlinear systems using natural language-based rules. It details the key concepts of fuzzy logic including linguistic variables, membership functions, fuzzy set operations, fuzzy inference systems and the 5-step fuzzy inference process of fuzzifying inputs, applying fuzzy operations and implications, aggregating outputs and defuzzifying results.
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
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.
This presentation educates you about AI - Fuzzy Logic Systems and its Implementation, Why Fuzzy Logic?, Why Fuzzy Logic?, Membership Function, Example of a Fuzzy Logic System and its Algorithm.
For more topics stay tuned with Learnbay.
---TABLE OF CONTENT---
Introduction
Differences between crisp sets & Fuzzy sets
Operations on Fuzzy Sets
Properties
MF formulation and parameterization
Fuzzy rules and Fuzzy reasoning
Fuzzy interface systems
Introduction to genetic algorithm
This presentation discusses the following Fuzzy logic concepts:
Introduction
Crisp Variables
Fuzzy Variables
Fuzzy Logic Operators
Fuzzy Control
Case Study
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.
The Fuzzy Logic is discussed with three simple example problems all solved in MATLAB
1. Restaurant Problem
2. Temperature Controller
3. Washing Machine Problem
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.
This presentation educates you about AI - Fuzzy Logic Systems and its Implementation, Why Fuzzy Logic?, Why Fuzzy Logic?, Membership Function, Example of a Fuzzy Logic System and its Algorithm.
For more topics stay tuned with Learnbay.
---TABLE OF CONTENT---
Introduction
Differences between crisp sets & Fuzzy sets
Operations on Fuzzy Sets
Properties
MF formulation and parameterization
Fuzzy rules and Fuzzy reasoning
Fuzzy interface systems
Introduction to genetic algorithm
This presentation discusses the following Fuzzy logic concepts:
Introduction
Crisp Variables
Fuzzy Variables
Fuzzy Logic Operators
Fuzzy Control
Case Study
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.
The Fuzzy Logic is discussed with three simple example problems all solved in MATLAB
1. Restaurant Problem
2. Temperature Controller
3. Washing Machine Problem
AN OPTIMUM TIME QUANTUM USING LINGUISTIC SYNTHESIS FOR ROUND ROBIN CPU SCHEDU...ijsc
In Round Robin CPU scheduling algorithm the main concern is with the size of time quantum and the increased waiting and turnaround time. Decision for these is usually based on parameters which are assumed to be precise. However, in many cases the values of these parameters are vague and imprecise.
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An Optimum Time Quantum Using Linguistic Synthesis for Round Robin Cpu Schedu...ijsc
In Round Robin CPU scheduling algorithm the main concern is with the size of time quantum and the increased waiting and turnaround time. Decision for these is usually based on parameters which are assumed to be precise. However, in many cases the values of these parameters are vague and imprecise. The performance of fuzzy logic depends upon the ability to deal with Linguistic variables. With this intent, this paper attempts to generate an Optimal Time Quantum dynamically based on the parameters which are treated as Linguistic variables. This paper also includes Mamdani Fuzzy Inference System using Trapezoidal membership function, results in LRRTQ Fuzzy Inference System. In this paper, we present an algorithm to improve the performance of round robin scheduling algorithm. Numerical analysis based on LRRTQ results on proposed algorithm show the improvement in the performance of the system by reducing unnecessary context switches and also by providing reasonable turnaround time.
Fuzzy soft set approach in decision making plays a crucial role by using Dempster–Shafer theory of evidence. First, the uncertain degrees of several parameters are obtained via grey relational analysis that apply to calculate the grey mean relational degree. Secondly, a mass functions of different independent choices with several parameters have given according to the uncertain degree. Lastly, aggregate the choices into a collective choices, Dempster’s rule of evidence combination have been utilized. The aforesaid soft computing based method have been applied on decision making problem.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
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This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
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Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
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Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
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DevOps and Testing slides at DASA ConnectKari Kakkonen
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GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
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Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
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CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
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Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...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
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Paper: https://eprint.iacr.org/2023/1886
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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. Content
What is Fuzzy?
Sets Theory
What is Fuzzy Logic?
Why use Fuzzy Logic?
Theory of Fuzzy Sets
Vocabulary
Fuzzy if-then Rules
Fuzzy Logic Operations
Fuzzy Inference Systems (FIS)
Fuzzy Inference Process
References
3. What is Fuzzy?
Fuzzy means
not clear, distinct or precise;
not crisp (well defined);
blurred (with unclear outline).
4. Sets Theory
Classical Set: An element either belongs or does not
belong to a sets that have been defined.
Fuzzy Set: An element belongs partially or gradually to
the sets that have been defined.
5. What is Fuzzy Logic?
It has two different meanings as,
In narrow sense: Fuzzy logic is a logical system,
which is an extension of multi-valued logic.
In a wider sense: Fuzzy logic (FL) is almost
synonymous with the theory of fuzzy sets, a theory
which relates to classes of objects with unsharp
boundaries in which membership is a matter of
degree.
Fuzzy logic (FL) should be interpreted in its wider
sense
6. What is Fuzzy Logic?
A way to represent variation or imprecision in logic
A way to make use of natural language in logic
Approximate reasoning
Definition of Fuzzy Logic:
A form of knowledge representation suitable for
notions that cannot be defined precisely, but which
depend upon their contexts.
Superset of conventional (Boolean) logic that has been
extended to handle the concept of partial truth - the truth
values between "completely true & completely false".
7. Why use Fuzzy Logic?
Conceptually easy to understand
Flexible
Tolerant of imprecise data
FL can model nonlinear functions of arbitrary complexity
FL can be built on top of the experience of experts
FL can be blended with conventional control techniques
FL is based on natural language
9. Theory of Fuzzy Sets
Theory which relates to classes of objects with unsharp
boundaries in which membership is matter of degree
Thus every problem can be presented in terms of
Fuzzy Sets
A set without crisp
Fuzzy set describes vague concepts
Fuzzy set admits the possibility of partial membership
in it
Degree of an object belongs to Fuzzy Set is denoted by
membership value between 0 to 1
Membership Function (MF) associated with a given
Fuzzy Set maps an input value to its appropriate
membership value
10. Vocabulary
Linguistic Variable: Variable whose values are words
or sentences rather than numbers
It represent qualities spanning a particular spectrum
Example: Speed, Service, Tip, Temperature, etc.
Linguistic Value or Term: Values or Terms used to
describe Linguistic Variable
Example: For Speed (Slowest, Slow, Fast, Fastest), For
Service (Poor, Good, Excellent), For Temperature
(Freezing, Cool, Warm, Hot), etc.
11. Vocabulary
Universe of Discourse or Universe or Input Space (U):
Set of all possible elements that can come into
consideration, confer the set U in (1).
It depends on context.
Elements of a fuzzy set are taken from a Universe of
Discourse.
An application of the universe is to suppress faulty
measurement data.
Example:
Set of x >> 1 could have as a universe of all real numbers,
alternatively all positive integer.
12. Vocabulary
Membership Function (MF) is a curve that defines how
each point in the input space is mapped to a membership
value between 0 and 1.
It is denoted by µ.
Membership value is also called as degree of membership
or membership grade or degree of truth of proposal.
Types of Membership Functions:
Piece-wise linear functions
Gaussian distribution function
Sigmoid curve
Quadratic and cubic polynomial curves
Singleton Membership Function
14. Syntax of Fuzzy Set
A = {x, µA(x) | x X}
Where,
A – Fuzzy Set
x – Elements of X
X – Universe of Discourse
µA(x) – Membership Function of x in A
15. Fuzzy if-then Rules
Statements used to formulate the conditional statements
that comprise fuzzy logic
Example:
if x is A then y is B
where,
A & B – Linguistic values
x – Element of Fuzzy set X
y – Element of Fuzzy set Y
In above example,
Antecedent (or Premise)– if part of rule (i.e. x is A)
Consequent (or Conclusion) – then part of rule (i.e. y is B)
Antecedent is interpretation & Consequent is assignment
16. Fuzzy if-then Rules
Antecedent is combination of proposals by AND, OR, NOT
operators
Consequent is combination of proposals linked by AND
operators. OR and NOT operators are not used in
consequents as these are cases of uncertainty.
Example:
If it is early, then John can study.
Universe: U = {4,8,12,16,20,24}; time of day
Input Fuzzy set: early = {(4,0),(8,1),(12,0.9),(16,0.7),(20,0.5),(24,0.2)}
Output Fuzzy set: can study=singleton Fuzzy set (assume) so study =1
i.e. at 20 (8 pm), early (20) = 0.5
17. Fuzzy if-then Rules
Interpreting if-then rule is a three–part process
1) Fuzzify Input: Resolve all fuzzy statements in the
antecedent to a degree of membership between 0 and 1.
2) Apply fuzzy operator to multiple part antecedents:
If there are multiple parts to the antecedent, apply fuzzy
logic operators and resolve the antecedent to a single
number between 0 and 1.
3) Apply implication method: The output fuzzy sets
for each rule are aggregated into a single output fuzzy
set. Then the resulting output fuzzy set is defuzzified, or
resolved to a single number.
19. Fuzzy Logic Operations
Fuzzy Logic Operators are used to write logic
combinations between fuzzy notions (i.e. to perform
computations on degree of membership)
Zadeh operators
1) Intersection: The logic operator corresponding to
the intersection of sets is AND.
(A AND B) = MIN( (A), (B))
2) Union: The logic operator corresponding to the
union of sets is OR.
(A OR B) = MAX( (A), (B))
3) Negation: The logic operator corresponding to the
complement of a set is the negation.
(NOT A) =1- (A)
21. Fuzzy Inference Systems (FIS)
Fuzzy Inference is the process of formulating the mapping
from a given input to an output using fuzzy logic.
Process of fuzzy inference involves Membership Functions
(MF), Logical Operations and If-Then Rules.
FIS having multidisciplinary nature, so cab called as
fuzzy-rule-based systems, fuzzy expert systems, fuzzy
modeling, fuzzy associative memory, fuzzy logic
controllers, and simply (and ambiguously) fuzzy systems.
Types of FIS:
1) Mamdani-type: Most commonly used. Expects the output MF’s to
be fuzzy sets.
2) Sugeno-type: Output MF’s are either linear or constant.
22. Fuzzy Inference Process
To describe the fuzzy inference process, lets consider the
example of two-input, one-output, two-rule valve control
problem.
23. Fuzzy Inference Process
Step 1: Fuzzify Input (Fuzzification)
Take the inputs and determine the degree to which they
belong to each of the appropriate fuzzy sets via
membership functions.
Input is always a crisp numerical value limited to the
universe of discourse of the input variable.
Output is a fuzzy degree of membership in the
qualifying linguistic set.
Each input is fuzzified over all the qualifying
membership functions required by the rules.
25. Fuzzy Inference Process
Step 2 : Apply Fuzzy Operator
If the antecedent of a given rule has more than one
part, the fuzzy operator is applied to obtain one
number that represents the result of the antecedent
for that rule.
The input to the fuzzy operator is two or more
membership values from fuzzified input variables.
The output is a single truth value.
27. Fuzzy Inference Process
Step 3: Apply Implication Method
First must determine the rule’s weight.
Operation in which the result of fuzzy operator is used to
determine the conclusion of the rule is called as
implication.
The input for the implication process is a single number
given by the antecedent.
The output of the implication process is a fuzzy set.
Implication is implemented for each rule.
29. Fuzzy Inference Process
Step 4 : Aggregate All Outputs
Aggregation is the process by which the fuzzy sets that
represent the outputs of each rule are combined into a
single fuzzy set.
Aggregation only occurs once for each output variable.
The input of the aggregation process is the list of
truncated output functions returned by the implication
process for each rule.
The output of the aggregation process is one fuzzy set
for each output variable.
31. Fuzzy Inference Process
Step 5: Defuzzify
Move from the “fuzzy world” to the “real world” is
known as defuzzification.
The input for the defuzzification process is a fuzzy set.
The output is a single number.
The most popular defuzzification method is the
centroid calculation, which returns the center of area
under the curve
Other methods are bisector, middle of maximum (the
average of the maximum value of the output set),
largest of maximum, and smallest of maximum.