1. Dr. Unnikrishnan P.C. Professor, EEE
EE368 Soft Computing
Dr. Unnikrishnan P.C.
Professor, EEE

2. Module I
Introduction to Soft Computing and Neural Networks

3. Introduction
Soft Computing refers to a consortium of computational methodologies like fuzzy logic, neural networks, genetic algorithms etc
All having their roots in the Artificial Intelligence
Artificial Intelligence is an area of computer science concerned with designing intelligent systems.
Systems that exhibit the characteristics we associate with intelligence in human behavior.

4. Intelligent systems (ISs)
Intelligence: System must
Perform meaningful operations.
Interpret information.
Comprehend the relations between phenomena or objects.
Apply the acquired information to new conditions.

5. Short-Term Objectives of ISs
Everyday routine tasks of human beings: Vision, language processing, common sense, reasoning, learning, robotics
Artificial routine tasks identified and developed by human beings: games, mathematics, logic, programming.
Expert tasks developed by human beings: Doctors, Physicists, Mechanical Engineers, accountants, other specializations.

6. Long Term Objectives of ISs
To develop a system which
can in essence be a replacement for human beings in difficult situations
can be physically merged with human beings to replace failed body parts or to create cyborgs

7. Cyborgs
Mostly Sci-fi

8. Traditional Approaches
Mathematical models: Black boxes, number crunching.
Rule-based systems (crisp & bivalent): Large rule bases.

9. Soft computing (SC)
Objective:
Mimic human (linguistic) reasoning

10. Soft Computing:Definition
(adapted from L.A. Zadeh)
• Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role model for soft computing is the human mind.

11. Soft Computing:Definition
(adapted from en.wikipedia.org)
Soft computing is a term applied to a field within computer science which is characterized by the use of inexact solutions to computationally-hard tasks such as the solution of NP-complete problems, for which an exact solution cannot be derived in polynomial time.

12. NP-Complete Problem
A problem which is both NP (verifiable in nondeterministic polynomial time) and NP-hard (any NP-problem can be translated into this problem). Examples of NP-hard problems include the Hamiltonian cycle and traveling salesman problems.

13. NP-Hard Problem
The traveling salesman problem is a problem in graph theory requiring the most efficient (i.e., least total distance) a salesman can take through each of  cities. No general method of solution is known, and the problem is NP-hard.

14. What is Hard Computing?
Hard computing, i.e., conventional computing, requires a precisely stated analytical model and often a lot of computation time.
Many analytical models are valid for ideal cases.
Real world problems exist in a non-ideal environment.

15. Hard Computing vs Soft Computing
Real-time constraints
Need of accuracy and precision in calculations and outcomes
Useful in critical systems
Soft computing
Soft constraints
Need of robustness rather than accuracy
Useful for routine tasks that are not critical

16. Hard Computing vs Soft Computing
Soft computing differs from conventional (hard) computing in that it is tolerant of the following
Imprecision
Uncertainty
Partial truth, and
Approximation.
In effect, the role model for soft computing is the human mind. The guiding principle of soft computing is:
Exploit the tolerance for imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness and low solution cost.

17. Constituents of SC Fuzzy Computing
Multi-valued Logic for treatment of imprecision and vagueness
Neural Computing
Neural Computers mimic certain processing capabilities of the human brain
Evolutionary Computation (Genetic Algorithms)
GAs are used to mimic some of the processes observed in natural evolution and GAs are used to evolve programs to perform certain tasks.
Support Vector Machines (SVM)
Probabilistic Reasoning (PR)

18. Advantages of SC Models based on human reasoning.
Closer to human thinking
Models can be
linguistic
simple (no number crunching),
comprehensible (no black boxes),
fast when computing,
effective in practice.

19. SC today (Zadeh) Computing with words (CW)
In computing with words and perceptions (CWP), the objects of computation are words, perceptions, and propositions drawn from a natural language.
Theory of information granulation (TFIG)
- inspired by the ways in which humans granulate information and reason with it.
Computational theory of perceptions (CTP)-decision-relevant information is a mixture of measurements and perceptions

20. Possible SC data & operations
Numeric data:
5, about 5, 5 to 6, about 5 to 6
Linguistic data:
cheap, very big, not high, medium or bad
Functions & relations:
f(x), about f(x), fairly similar, much greater

21. Neural networks (NN, 1940's)
Neural networks offer a powerful method to explore, classify, and identify patterns in data.
Neuron: y=wixi
Walter Pitts
Neurons (1 layer)
Inputs
Outputs
Warren S. McCulloch

23. Machine learning (supervised)
A ‘teacher ‘ is assumed to be present during the learning process.
Pattern recognition based on training data.
Classification supervised by instructor.
Neural (crisp or fuzzy), neuro-fuzzy and fuzzy models.

24. Supervised Learning

25. Supervised Learning

26. Supervised Learning-Regression

27. Regression - Example

28. Regression Applications

29. Unsupervised Learning
There is no teacher present to hand over the desired output and the network therefore tries to learn by itself organizing the input instances of the problem.
Pattern recognition based on training data.
Classification based on structure of data (clustering).
No instructor
Neural (crisp or fuzzy), neuro-fuzzy and fuzzy models.

30. Unsupervised Learning

31. Unsupervised Learning-Applications

32. Reinforcement Learning

33. Reinforcement Learning-Example

34. Fuzzy systems (Zadeh, 1960's)
Deal with imprecise entities in automated environments (computer environments)
Based on fuzzy set theory and fuzzy logic.
Most applications in control and decision making
Lotfi A. Zadeh
Omron’s fuzzy processor

35. SC applications: control
Heavy industry
Matsushita, Siemens
robotic arms, humanoid robots 
Home appliances 
Canon, Sony, Goldstar, Siemens
washing machines, ACs, refrigerators, cameras 
Automobiles
Nissan, Mitsubishi, Daimler- Chrysler, BMW, Volkswagen
Travel Speed Estimation, Sleep Warning Systems, Driver-less cars  
Spacecrafts 
NASA
Manoeuvering of a Space Shuttle(FL), Optimization of Fuel- efficient Solutions for a Manoeuvre(GA), Monitoring and Diagnosis of Degradation of Components and Subsystems(FL), Virtual Sensors(ANN) 

36. SC applications: Business
Knowledge-based prognosis,
Fuzzy data analysis
Hospital stay prediction,
TV commercial slot evaluation,
address matching,
fuzzy cluster analysis,
sales prognosis for mail order house.
(source: FuzzyTech)
Supplier evaluation for sample testing,
Customer targeting,
Sequencing,
Scheduling,
Optimizing R&D
Projects,

37. SC applications: Finance
Fuzzy scoring for mortgage applicants,
Creditworthiness assessment,
Fuzzy-enhanced score card for lease risk assessment,
Risk profile analysis,
Insurance fraud detection,
Cash supply optimization,
Foreign exchange trading,
Trading surveillance,
Investor classification etc. Source: FuzzyTech

38. SC Applications: Robotics

39. SC applications: others
Statistics
Social sciences
Behavioural sciences
Biology
Medicine

40. Thank You