The document discusses fuzzy expert systems, including their components, knowledge representation, and applications. It describes fuzzy expert systems as using fuzzy rules, fuzzy logic, and fuzzy sets to represent knowledge. The key components are a knowledge base containing fuzzy production rules, an inference engine that uses fuzzy logic to make inferences, and an interface that allows users to engage with the system. Fuzzy expert systems can be applied in various domains like agriculture, sports, and engineering to provide expert advice and solutions.

1. SUBMITTED BY-BISWARUP DAS
SEMESTER-10TH
ROLL NO-04
PRESENTATION
ON
FUZZY EXPERT SYSTEM

2. *INTRODUCTION
*OVERVIEW OF EXPERT SYSTEM
*COMPONENTS OF FUZZY EXPERT SYSTEM
*FUZZY RULES
*LINGUISTIC VARIABLES
*FUZZY EXPERT SYSTEM
*CONSTRUCTION OF FUZZY EXPERT SYSTEM
*DESIGN PROCEDURE
*APPLICATIONS
*CONCLUSION
*REFERENCES

3. *According to Webster an EXPERT is –
“One with special skill or knowledge representing mastery of
particular subject”.
In every domain, there exist someone who has adequate
knowledge about certain parts of that domain.
However it is not possible for every person to master every
knowledge of that domain.
Experts is being defined as per their capabilities to solve
particular task of that domain.

4. *Problems that are often tackled by human experts are handled
by expert systems.
*It requires the following:-
- a substantial knowledge base
- a good inference engine
- an effective user interface that can engage with
consumers to address issues related to the
specific area for which it was created.
*It acquires knowledge from human experts through the
knowledge acquisition process.

7. *Knowledge base:- It contains the fuzzy production rules(e.g.-if A
then B).
*Inference Engine:- In this, it uses two approaches to make fuzzy
inferences based on a set of production rules:-
- Data driven
- Goal driven
*Meta knowledge base:- Basically, it includes guidelines for using
production rules in the knowledge base.
*Explanatory Interface:- It creates a channel of communication
between the user and the system and outlines how the system will
provide the user with a solution to their problem.
*Knowledge Acquisition Module:- It obtains relevant knowledge from
human experts in order to update the knowledge and meta
knowledge base.

8. *A fuzzy rule is a conditional statement in the form of:
- IF x is A
- THEN y is B
* x and y are linguistic variables.
*A and B are linguistic values determined by fuzzy sets
on the universe of discourses X and Y respectively.

9. *A linguistic variable is fuzzy variable
-e.g. the fact “Jack is tall” implies linguistic variable
“Jack” takes the linguistic value “tall”.
*Use linguistic variables to form fuzzy rules:
- If ‘project duration’ is long
THEN ‘risk’ is high
- If risk is very high
THEN ‘project funding’ is very low

10. *A fuzzy expert system is an expert system that uses
fuzzy rules, fuzzy logic, and fuzzy sets.
*A fuzzy logic system will occasionally activate many
rules
- If the antecedent is true to some degree of
membership, then the consequent is true to same
degree.

11. *Two distinct fuzzy sets describing tall and heavy:

12. IF height is tall
THEN weight is heavy

13. *Other Examples (multiple antecedents)
-e.g. IF ‘Project duration’ is long
AND ‘Project staffing’ is large
AND ‘Project funding’ is inadequate
THEN risk is high
*e.g. IF service is excellent
OR food is delicious
THEN tip is generous

14. *Knowledge Representation:-
-- knowledge can be represented by three ways-
1. Rules: If PREMISE then CONCLUSION
2. SEMANTIC NET: Class of knowledge representation formalism
using nodes and arcs.
3.Frame: Using slots and values, a data structure can represent
typical scenarios .
*Inference Engine:- Uses knowledge in a specific representation to
reach an expert conclusion or provide expert advice.
-- Operates on two basic ways-
1) Forward Chaining: Data Driven(e.g. XCON)
2) Backward Chaining: Goal Driven(e.g. MYCIN)

15. *Starting with some level of preparation and pre-processing is
necessary to identify the issue.
*Next, inference method has to be determined. Basically there are
two methods-
1) Mamdani Method
2) Takegi- sugeno- kang Method
*The choice of inference method is entirely dependent on the choice
of defuzzification method.
*The process of enumerating linguistic variable is to be carried out
after these selections have been made.
*Next stage is to determine membership functions and the fuzzy rules
that maps fuzzy facts to fuzzy conclusions.
*After creating the rules, they are tested against some desired
outputs in order to do optimization and accuracy.

16. *Agricultural Field:-
- VARIEX:- It enables selection of best cultivators for diverse
agricultural situations.
*Sports:-
-Goalkeeper quality recognition(Bazmara, jafari).
*Computer Engineering:-
- Fuzzy Controllers.
*Mechanical Engineering:-
- Emulation of complex production system.

17. *Fuzzy expert system are one of the most significant game changers
in the field of computation.
*However, it has a number of drawbacks and difficulties.
*To design a system that overcomes these drawbacks and provides an
efficient solution regarding its task domain is a very important and
challenging task.
*If we can able to do so, then it will provide an unmatched power to
the problem solving domain.

18. *Rich, Elain; Knight, kevin; Artificial Intelligence, Third edition.
*Klir, George J,; Yuan, Bo; fuzzy sets and Fuzzy Logic –Theory
and applications.
*Garibaldi, Jonathan M.; Fuzzy Expert Systems.
*Kandel, Abraham ; Fuzzy Expert Systems; CRC press.
*Web References
*https://www.merriam-webster.com/dictionary/expert