This document discusses defuzzification in fuzzy logic. It defines defuzzification as the process of converting fuzzy quantities into crisp quantities. There are several reasons for and applications of defuzzification, such as converting fuzzy controller outputs into crisp values for applications. The document outlines the defuzzification process and several common defuzzification methods, including the centroid method, weighted average method, and max membership principle. It also discusses the lambda-cut and alpha-cut methods for deriving crisp values from fuzzy sets and relations.
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.
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.
The Fuzzy Logic is discussed with three simple example problems all solved in MATLAB
1. Restaurant Problem
2. Temperature Controller
3. Washing Machine Problem
FPGA based Efficient Interpolator design using DALUT Algorithmcscpconf
Interpolator is an important sampling device used for multirate filtering to provide signal processing in wireless communication system. There are many applications in which sampling rate must be changed. Interpolators and decimators are utilized to increase or decrease the sampling rate. In this paper an efficient method has been presented to implement high speed and area efficient interpolator for wireless communication systems. A multiplier less technique is used which substitutes multiplyand-accumulate operations with look up table (LUT) accesses. Interpolator has been implemented using Partitioned distributed arithmetic look up table (DALUT) technique. This technique has been used to take an optimal advantage of embedded LUTs of the target FPGA. This method is useful to enhance the system performance in terms of speed and area. The proposed interpolator has been designed using half band poly phase FIR structure with Matlab, simulated with ISE, synthesized with Xilinx Synthesis Tools (XST) and implemented on Spartan-3E and Virtex2pro device. The proposed LUT based multiplier less approach has shown a maximum operating frequency of 92.859 MHz with Virtex Pro and 61.6 MHz with Spartan 3E by consuming considerably less resources to provide cost effective solution for wireless communication systems.
The Fuzzy Logic is discussed with three simple example problems all solved in MATLAB
1. Restaurant Problem
2. Temperature Controller
3. Washing Machine Problem
FPGA based Efficient Interpolator design using DALUT Algorithmcscpconf
Interpolator is an important sampling device used for multirate filtering to provide signal processing in wireless communication system. There are many applications in which sampling rate must be changed. Interpolators and decimators are utilized to increase or decrease the sampling rate. In this paper an efficient method has been presented to implement high speed and area efficient interpolator for wireless communication systems. A multiplier less technique is used which substitutes multiplyand-accumulate operations with look up table (LUT) accesses. Interpolator has been implemented using Partitioned distributed arithmetic look up table (DALUT) technique. This technique has been used to take an optimal advantage of embedded LUTs of the target FPGA. This method is useful to enhance the system performance in terms of speed and area. The proposed interpolator has been designed using half band poly phase FIR structure with Matlab, simulated with ISE, synthesized with Xilinx Synthesis Tools (XST) and implemented on Spartan-3E and Virtex2pro device. The proposed LUT based multiplier less approach has shown a maximum operating frequency of 92.859 MHz with Virtex Pro and 61.6 MHz with Spartan 3E by consuming considerably less resources to provide cost effective solution for wireless communication systems.
FPGA based Efficient Interpolator design using DALUT Algorithmcscpconf
Interpolator is an important sampling device used for multirate filtering to
provide signal processing in wireless communication system. There are many
applications in which sampling rate must be changed. Interpolators and decimators are
utilized to increase or decrease the sampling rate. In this paper an efficient method has
been presented to implement high speed and area efficient interpolator for wireless
communication systems. A multiplier less technique is used which substitutes multiplyand-accumulate
operations with look up table (LUT) accesses. Interpolator has been
implemented using Partitioned distributed arithmetic look up table (DALUT)
technique. This technique has been used to take an optimal advantage of embedded
LUTs of the target FPGA. This method is useful to enhance the system performance in
terms of speed and area. The proposed interpolator has been designed using half band
poly phase FIR structure with Matlab, simulated with ISE, synthesized with Xilinx
Synthesis Tools (XST) and implemented on Spartan-3E and Virtex2pro device. The
proposed LUT based multiplier less approach has shown a maximum operating
frequency of 92.859 MHz with Virtex Pro and 61.6 MHz with Spartan 3E by
consuming considerably less resources to provide cost effective solution for wireless
communication systems.
Word embeddings are common for NLP tasks, but embeddings can also be used to learn relations among categorical data. Deep learning can be useful also for structured data, and entity embeddings is one reason why it makes sense. These are slides from a seminar held in Sbanken.
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FAST ALGORITHMS FOR UNSUPERVISED LEARNING IN LARGE DATA SETScsandit
The ability to mine and extract useful information automatically, from large datasets, is a
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AI optimizing HPC simulations (presentation from 6th EULAG Workshop)byteLAKE
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In this paper, implementations of three Hough Transform based fingerprint alignment algorithms are analyzed with respect to time complexity on Java Card environment. Three algorithms are: Local Match Based Approach (LMBA), Discretized Rotation Based Approach
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A study on “Diagnosis Test of Diabetics and Hypertension by AI”, Presentation slides for International Conference on "Life Sciences: Acceptance of the New Normal", St. Aloysius' College, Jabalpur, Madhya Pradesh, India, 27-28 August, 2021
A study on “impact of artificial intelligence in covid19 diagnosis”Dr. C.V. Suresh Babu
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Although the lungs are one of the most vital organs in the body, they are vulnerable to infection and injury. COVID-19 has put the entire world in an unprecedented difficult situation, bringing life to a halt and claiming thousands of lives all across the world. Medical imaging, such as X-rays and computed tomography (CT), is essential in the global fight against COVID-19, and newly emerging artificial intelligence (AI) technologies are boosting the power of imaging tools and assisting medical specialists. AI can improve job efficiency by precisely identifying infections in X-ray and CT images and allowing further measurement. We focus on the integration of AI with X-ray and CT, both of which are routinely used in frontline hospitals, to reflect the most recent progress in medical imaging and radiology combating COVID-19.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
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2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
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The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
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1. Department of Information Technology 1Soft Computing (ITC4256 )
Dr. C.V. Suresh Babu
Professor
Department of IT
Hindustan Institute of Science & Technology
DEFUZZIFICATION
2. Department of Information Technology 2Soft Computing (ITC4256 )
Action Plan
• Defuzzification
• Why defuzzification?
• Defuzzification applications
• Defuzzification process
• Lambda-cut method
• Defuzzification methods
• Quiz at the end of session`
3. Department of Information Technology 3Soft Computing (ITC4256 )
FUZZY LOGIC CRISP LOGIC
In fuzzy logic we can take intermediate value between 0
and 1
Elements are allowed to be partially included in set
Used in Fuzzy Controllers.
It has infinite value
It can deal with representation of human intelligence.
Test Yourself
4. Department of Information Technology 4Soft Computing (ITC4256 )
FUZZY LOGIC CRISP LOGIC
In fuzzy logic we can take intermediate value between 0
and 1
in crisp logic we can take binary value either 0 or 1 (True
or False).
Elements are allowed to be partially included in set Elements is either the member of a set or not
Used in Fuzzy Controllers. Used in Digital Design.
It has infinite value It has Bi-valued.
It can deal with representation of human intelligence. It can’t deal with representation of human intelligence.
Answers
5. Department of Information Technology 5Soft Computing (ITC4256 )
DEFUZZIFICATION
Defuzzification means the fuzzy to crisp conversion.
Defuzzification is a mapping process from a space of fuzzy control actions defined over an
output universe of discourse into a space of crisp (nonfuzzy) control actions.
Defuzzification is a process of converting output fuzzy variable into a unique number.
Defuzzification process has the capability to reduce a fuzzy set into a crisp single-valued
quantity or into a crisp set; to convert a fuzzy matrix into a crisp matrix; or to convert a
fuzzy number into a crisp number.
5
6. Department of Information Technology 6Soft Computing (ITC4256 )
Why defuzzification?
• The fuzzy results generated can not be used in an application,
where decision has to be taken only on crisp values.
7. Department of Information Technology 7Soft Computing (ITC4256 )
Defuzzification applications
• In many practical applications, a control command is given as a
crisp value.
• a process to get a non-fuzzy control action that best represents
the possibility distribution of an inferred fuzzy control action.
• no systematic procedure for choosing a good defuzzification
strategy,
• select one in considering the properties of application case
8. Department of Information Technology 8Soft Computing (ITC4256 )
Defuzzification process
Defuzzification is the process of
conversion of fuzzy quantity into a
precise quantity.
• [A] first part of fuzzy output (C1)
• [B] Second part of fuzzy output (C2)
• [C] Union of part [A] and [B].
The union of two membership
function in values the max operator,
which is going to be the outer
envelope of the two or more shapes
9. Department of Information Technology 9Soft Computing (ITC4256 )
Lambda-cut method
• Lmabda-cut method is applicable to derive crisp value of a
fuzzy set or relation.
– Thus Lambda-cut method for fuzzy set
– Lambda-cut method for fuzzy relation
• In many literature, Lambda-cut method is also alternatively
termed as Alpha-cut method.
10. Department of Information Technology 10Soft Computing (ITC4256 )
Lamda-cut method for fuzzy set
• In this method a fuzzy set A is transformed into a crisp set A for
a given value of
• In other-words,
• That is, the value of Lambda-cut set A is x, when the
membership value corresponding to x is greater than or equal
to the specified .
• This Lambda-cut set A is also called alpha-cut set.
11. Department of Information Technology 11Soft Computing (ITC4256 )
Defuzzification methods include:
[1] max membership principle.
[2] centroid method.
[3] weighted average method.
[4] mean max membership.
[5] center of sums.
[6] centre of largest area.
[7] first of maxima, last of maxima.
12. Department of Information Technology 12Soft Computing (ITC4256 )
[1] Max – membership principle:
M c (x*) > M c (x) for all x ∈ X
13. Department of Information Technology 13Soft Computing (ITC4256 )
[2] Centroid method
• centre of mall, centre of gravity or area.
XA= ∫Ms(x).x.dx
∫Mc(x).dx
14. Department of Information Technology 14Soft Computing (ITC4256 )
[3] Weighted average method
Valid for symmetrical output membership function.
Each membership function is weighted by its max membership value.
15. Department of Information Technology 15Soft Computing (ITC4256 )
[4] Mean max membership method:
• This is known as middle of the maxima.
16. Department of Information Technology 16Soft Computing (ITC4256 )
5] Centre of sums:
Algebraic sum of
individual fuzzy the
union, here,
interesting areas are
value twice, the
defuzzified value X+
17. Department of Information Technology 17Soft Computing (ITC4256 )
[6] Centre of largest area
When output consists of at least two
converse fuzzy subsets which are not
overlapping. When o/p fuzzy set has
at least two converse regions, then
the centre of gravity of converse
fuzzy sub region having the largest
area is used to obtain defuzzified
value.
18. Department of Information Technology 18Soft Computing (ITC4256 )
[7] first of maxima (last of maxima)
• This method uses the overall output or union
of all individual output fuzzy sets ci for
determining the smallest value of the domain
maximized membership in ci
19. Department of Information Technology 19Soft Computing (ITC4256 )
Test Yourself1.Fuzzy logic is :
a) Used to respond to questions in a humanlike way
b) A new programming language used to program animation
c) The result of fuzzy thinking
d) A term that indicates logical values greater than one
2. Which of the following is not a part of fuzzy logic Systems
Architecture?
A. Fuzzification Module
B. Knowledge Base
C. Defuzzification Module
D. Interference base
3. The 7 Defuzzification methods are:
20. Department of Information Technology 20Soft Computing (ITC4256 )
Answers
1.Fuzzy logic is :
a) Used to respond to questions in a humanlike way
b) A new programming language used to program animation
c) The result of fuzzy thinking
d) A term that indicates logical values greater than one
2. Which of the following is not a part of fuzzy logic Systems
Architecture?
A. Fuzzification Module
B. Knowledge Base
C. Defuzzification Module
D. Interference base
3. The 7 Defuzzification methods are:
[1] max membership principle.
[2] centroid method.
[3] weighted average method.
[4] mean max membership.
[5] center of sums.
[6] centre of largest area.
[7] first of maxima, last of maxima.