Introduction to Soft Computing

1. MTCSCS302: SOFT COMPUTING
Dr. Sandeep Kumar Poonia
Associate Professor,
Faculty of Engineering & Technology,
Jagannath University, Jaipur
sandpoonia@gmail.com
August 28, 2016
Dr. Sandeep Kumar Poonia SOFT COMPUTING(MTCSCS302) 1/25

2. Syllabus
Books
Introduction to SC
Fuzzy Logic
Neural Networks
Probabilistic Reasoning
Outline
1 Syllabus
2 Books
3 Introduction to SC
4 Fuzzy Logic
5 Neural Networks
6 Probabilistic Reasoning
Dr. Sandeep Kumar Poonia SOFT COMPUTING(MTCSCS302) 2/25

3. Syllabus
Books
Introduction to SC
Fuzzy Logic
Neural Networks
Probabilistic Reasoning
Objective
UNIT I
UNIT II
UNIT III
UNIT IV
UNIT V
Objective & Description
Objective:To give students knowledge of soft computing theories
fundamentals, i.e. of fundamentals of non-traditional technologies and
approaches to solving hard real-world problems, namely of
fundamentals of artificial neural networks, fuzzy sets and fuzzy logic
and genetic algorithms.
Description:Soft computing covers non-traditional technologies or
approaches for solving hard real-world problems. Content of course, in
accordance with meaning of its name, is as follow: Tolerance of
imprecision and uncertainty as the main attributes of soft computing
theories. Neural networks. Fuzzy logic. Genetic algorithms.
Probabilistic reasoning. Rough sets. Chaos. Hybrid approaches
(combinations of neural networks, fuzzy logic and genetic algorithms).
Dr. Sandeep Kumar Poonia SOFT COMPUTING(MTCSCS302) 3/25

4. Syllabus
Books
Introduction to SC
Fuzzy Logic
Neural Networks
Probabilistic Reasoning
Objective
UNIT I
UNIT II
UNIT III
UNIT IV
UNIT V
UNIT I
Neural Networks: History, overview of biological Neuro-system,
Mathematical Models of Neurons, ANN architecture, Learning
rules, Learning Paradigms-Supervised, unsupervised and
reinforcement Learning, ANN training Algorithms-perceptions,
Training rules, Delta, Back Propagation Algorithm, Multilayer
Perceptron Model, Hopfield Networks, Associative Memories,
Applications of Artificial Neural Networks.
Dr. Sandeep Kumar Poonia SOFT COMPUTING(MTCSCS302) 4/25

5. Syllabus
Books
Introduction to SC
Fuzzy Logic
Neural Networks
Probabilistic Reasoning
Objective
UNIT I
UNIT II
UNIT III
UNIT IV
UNIT V
UNIT II
Fuzzy Logic: Introduction to Fuzzy Logic, Classical and Fuzzy
Sets: Overview of Classical Sets, Membership Function, Fuzzy
rule generation. Operations on Fuzzy Sets: Compliment,
Intersections, Unions, Combinations of Operations, Aggregation,
Operations.
Dr. Sandeep Kumar Poonia SOFT COMPUTING(MTCSCS302) 5/25

6. Syllabus
Books
Introduction to SC
Fuzzy Logic
Neural Networks
Probabilistic Reasoning
Objective
UNIT I
UNIT II
UNIT III
UNIT IV
UNIT V
UNIT III
Fuzzy Arithmetic: Fuzzy Numbers, Linguistic Variables,
Arithmetic Operations on Intervals & Numbers, Lattice of Fuzzy
Numbers, Fuzzy Equations. Fuzzy Logic: Classical Logic,
Multivalued Logics, Fuzzy Propositions, Fuzzy
Qualifiers,Uncertainty based Information: Information &
Uncertainty, Nonspecificity of Fuzzy & Crisp Sets, Fuzziness of
Fuzzy Sets.
Dr. Sandeep Kumar Poonia SOFT COMPUTING(MTCSCS302) 6/25

7. Syllabus
Books
Introduction to SC
Fuzzy Logic
Neural Networks
Probabilistic Reasoning
Objective
UNIT I
UNIT II
UNIT III
UNIT IV
UNIT V
UNIT IV
Introduction of Neuro-Fuzzy Systems: Architecture of Neuro
Fuzzy Networks. Application of Fuzzy Logic: Medicine,
Economics etc.
Dr. Sandeep Kumar Poonia SOFT COMPUTING(MTCSCS302) 7/25

8. Syllabus
Books
Introduction to SC
Fuzzy Logic
Neural Networks
Probabilistic Reasoning
Objective
UNIT I
UNIT II
UNIT III
UNIT IV
UNIT V
UNIT V
Algorithms: An overview of Genetic Algorithm, Artificial Bee
Colony Algorithm, Ant Colony Algorithm etc. Applications and
implementation of these algorithms.
Dr. Sandeep Kumar Poonia SOFT COMPUTING(MTCSCS302) 8/25

9. Syllabus
Books
Introduction to SC
Fuzzy Logic
Neural Networks
Probabilistic Reasoning
Books
Books
Vijay Lakshmi, Pai, Neural Networks, Fuzzy Logic and Genetic
Algorithms, Soft Computing Paradigms, Prentice Hall of India
(2008).
Timothy Ross, Fuzzy Logic, Wiley India (2007) 2nd ed.
F. O. Karray and C. de Silva, Soft computing and Intelligent
System Design, Pearson, 2009.
G.J. Klir & B. Yuan, Fuzzy Sets & Fuzzy Logic, PHI, 1995.
Hertz J. Krogh, R.G. Palmer, Introduction to the Theory of
Neural Computation, Addison-Wesley, California, 1991.
Dr. Sandeep Kumar Poonia SOFT COMPUTING(MTCSCS302) 9/25

10. Syllabus
Books
Introduction to SC
Fuzzy Logic
Neural Networks
Probabilistic Reasoning
Introduction to SC
Goal of Soft Computing
AI v/s CI
Soft Computing
The term soft computing was proposed by the inventor of fuzzy
logic, Lotfi A. Zadeh. He describes it as follows :
Soft computing is a collection of methodologies that aim to
exploit the tolerance for imprecision and uncertainty to
achieve tractability, robustness, and low solution cost. Its
principal constituents are fuzzy logic, neurocomputing, and
probabilistic reasoning. Soft computing is likely to play an
increasingly important role in many application areas,
including software engineering. The role model for soft
computing is the human mind.
Dr. Sandeep Kumar Poonia SOFT COMPUTING(MTCSCS302) 10/25

11. Syllabus
Books
Introduction to SC
Fuzzy Logic
Neural Networks
Probabilistic Reasoning
Introduction to SC
Goal of Soft Computing
AI v/s CI
Soft Computing...
Soft Computing is an emerging (up and coming, rising,
promising, talented) approach to computing which parallel the
remarkable ability of human mind to reason and learn in a
environment of uncertainty (doubt) and imprecision.
Zadeh defines SC into one multidisciplinary system as the fusion
(Union or Combination) of the fields of Fuzzy Logic,
Neuro-Computing, Genetic Computing and Probabilistic
Computing.
Fusion of methodologies designed to model and enable
solutions to real world problems, which are not modeled or too
difficult to model mathematically.
Dr. Sandeep Kumar Poonia SOFT COMPUTING(MTCSCS302) 11/25

12. Syllabus
Books
Introduction to SC
Fuzzy Logic
Neural Networks
Probabilistic Reasoning
Introduction to SC
Goal of Soft Computing
AI v/s CI
Soft Computing...
SC consist of : Neural Networks, Fuzzy Systems, and Genetic
Algorithms.
Neural Networks: for learning and adaption.
Fuzzy Systems: for knowledge representation via fuzzy if-then
rules.
Genetic Algorithms: for evolutionary computation.
Soft Computing is still growing and developing.
Dr. Sandeep Kumar Poonia SOFT COMPUTING(MTCSCS302) 12/25

13. Syllabus
Books
Introduction to SC
Fuzzy Logic
Neural Networks
Probabilistic Reasoning
Introduction to SC
Goal of Soft Computing
AI v/s CI
Soft Computing...
Soft Computing as an attempt to mimic natural creatures: plants, animals,
human beings, which are soft, flexible, adaptive and clever. In this sense soft
computing is the name of a family of problem-solving methods that have analogy
with biological reasoning and problem solving (sometimes referred to as
cognitive computing).
The basic methods included in cognitive computing are fuzzy logic, neural
networks and GA - the methods which do not derive from classical theories.
Soft computing can also be seen as a foundation for the growing field of
computational intelligence (CI). The difference between traditional AI and CI is
that AI is based on hard computing whereas CI is based on soft computing.
Soft Computing is not just a mixture of these ingredients, but a discipline in which
each constituent contributes a distinct methodology for addressing problems in
its domain, in a complementary rather than competitive way.
Dr. Sandeep Kumar Poonia SOFT COMPUTING(MTCSCS302) 13/25

14. Syllabus
Books
Introduction to SC
Fuzzy Logic
Neural Networks
Probabilistic Reasoning
Introduction to SC
Goal of Soft Computing
AI v/s CI
Goal of Soft Computing
It is a new multidisciplinary field, to construct a new generation
of Artificial Intelligence, known as Computational Intelligence.
The main goal is: to develop intelligent machines to provide
solutions to real world problems, which are not modeled or too
difficult to model mathematically.
Its aim is to exploit (develop) the tolerance for Approximation,
Uncertainty, Imprecision, and Partial Truth in order to achieve
close resemblance with human like decision making.
Dr. Sandeep Kumar Poonia SOFT COMPUTING(MTCSCS302) 14/25

15. Syllabus
Books
Introduction to SC
Fuzzy Logic
Neural Networks
Probabilistic Reasoning
Introduction to SC
Goal of Soft Computing
AI v/s CI
AI v/s CI
AI: AI is the intelligence exhibited by machines or software. It is also an
academic field of study. Major AI researchers and textbooks define the field as
”the study and design of intelligent agents”,where an intelligent agent is a system
that perceives its environment and takes actions that maximize its chances of
success.
CI: CI is a set of nature-inspired computational methodologies and approaches
to address complex real-world problems to which traditional approaches, first
principles modeling or explicit statistical modeling, are ineffective or infeasible.
Many such real-life problems are not considered to be well-posed problems
mathematically, but nature provides many counterexamples of biological systems
exhibiting the required function, practically.
CI is an offshoot of AI in which the emphasis is placed on heuristic algorithms
such as fuzzy systems, neural networks and evolutionary computation.
Dr. Sandeep Kumar Poonia SOFT COMPUTING(MTCSCS302) 15/25

16. Syllabus
Books
Introduction to SC
Fuzzy Logic
Neural Networks
Probabilistic Reasoning
Introduction to Fuzzy Logic
Fuzzy Logic
Characteristics of Fuzzy Logic
Applications of Fuzzy Logic
The basic methods included in Soft Computing are fuzzy logic
(FL), neural networks (NN) and genetic algorithms (GA) - the
methods which do not derive from classical theories.
Fuzzy logic is mainly associated to imprecision, approximate
reasoning and computing with words,
neurocomputing to learning and curve fitting (also to
classification), and
probabilistic reasoning to uncertainty and belief propagation
(belief networks).
Dr. Sandeep Kumar Poonia SOFT COMPUTING(MTCSCS302) 16/25

17. Syllabus
Books
Introduction to SC
Fuzzy Logic
Neural Networks
Probabilistic Reasoning
Introduction to Fuzzy Logic
Fuzzy Logic
Characteristics of Fuzzy Logic
Applications of Fuzzy Logic
These methods have in common that they
are nonlinear,
have ability to deal with non-linearities,
follow more human-like reasoning paths than classical
methods,
utilize self-learning,
utilize yet-to-be-proven theorems,
are robust in the presence of noise or errors.
Dr. Sandeep Kumar Poonia SOFT COMPUTING(MTCSCS302) 17/25

18. Syllabus
Books
Introduction to SC
Fuzzy Logic
Neural Networks
Probabilistic Reasoning
Introduction to Fuzzy Logic
Fuzzy Logic
Characteristics of Fuzzy Logic
Applications of Fuzzy Logic
Fuzzy set theory was developed by Lotfi A. Zadeh, professor for
computer science at the University of California in Berkeley, to
provide a mathematical tool for dealing with the concepts used in
natural language (linguistic variables). Fuzzy Logic is basically a
multivalued logic that allows intermediate values to be defined
between conventional evaluations.
The developement of fuzzy logic was motivated in large measure
by the need for a conceptual frame work which can address the
issue of uncertainty and lexical imprecision.
Dr. Sandeep Kumar Poonia SOFT COMPUTING(MTCSCS302) 18/25

19. Syllabus
Books
Introduction to SC
Fuzzy Logic
Neural Networks
Probabilistic Reasoning
Introduction to Fuzzy Logic
Fuzzy Logic
Characteristics of Fuzzy Logic
Applications of Fuzzy Logic
Some of the essential characteristics of fuzzy logic relate to the
following:
In fuzzy logic, exact reasoning is viewed as a limiting case
of approximate reasoning.
In fuzzy logic, everything is a matter of degree.
In fuzzy logic, knowledge is interpreted a collection of
elastic or, equivalently, fuzzy constraint on a collection of
variables.
Inference is viewed as a process of propagation of elastic
constraints.
Any logical system can be fuzzified.
Dr. Sandeep Kumar Poonia SOFT COMPUTING(MTCSCS302) 19/25

20. Syllabus
Books
Introduction to SC
Fuzzy Logic
Neural Networks
Probabilistic Reasoning
Introduction to Fuzzy Logic
Fuzzy Logic
Characteristics of Fuzzy Logic
Applications of Fuzzy Logic
Applications
The most significant application area of fuzzy logic has been in control field. It
has been made a rough guess that 90% of applications are in control.
Fuzzy control includes fans, complex aircraft engines and control surfaces,
helicopter control, missile guidance, automatic transmission, wheel slip control,
industrial processes and so on.
Commercially most significant have been various household and entertainment
electronics, for example washing machine controllers and autofocus cameras.
The most famous controller is the subway train controller in Sengai, Japan.
Fuzzy system performs better (uses less fuel, drives smoother) when compared
with a conventional PID controller.
Companies that have fuzzy research are General Electric, Siemens, Nissan,
Mitsubishi, Honda, Sharp, Hitachi, Canon, Samsung, Omron, Fuji, McDonnell
Douglas, Rockwell, etc.
Dr. Sandeep Kumar Poonia SOFT COMPUTING(MTCSCS302) 20/25

21. Syllabus
Books
Introduction to SC
Fuzzy Logic
Neural Networks
Probabilistic Reasoning
Introduction to Neural Networks
Neural Networks..
NN Characteristics
A computer system modelled on the human brain and nervous
system.
Neural network makes an attempt to simulate human brain. The
simulating is based on the present knowledge of brain function,
and this knowledge is even at its best primitive.
So, it is not absolutely wrong to claim that artificial neural
networks probably have no close relationship to operation of
human brains. The operation of brain is believed to be based on
simple basic elements called neurons which are connected to
each other with transmission lines called axons and receptive
lines called dendrites.
The learning may be based on two mechanisms: the creation of
new connections, and the modification of connections. Each
neuron has an activation level which, in contrast to Boolean
logic, ranges between some minimum and maximum value.
Dr. Sandeep Kumar Poonia SOFT COMPUTING(MTCSCS302) 21/25

22. Syllabus
Books
Introduction to SC
Fuzzy Logic
Neural Networks
Probabilistic Reasoning
Introduction to Neural Networks
Neural Networks..
NN Characteristics
NN are simplified models of the biological neuron system.
Neural network: information processing paradigm(model)
inspired by biological nervous systems, such as our brain
Structure: large number of highly interconnected processing
elements (neurons) working together. Inspired by brain.
Like people, they learn from experience (by example), therefore
train with known example of problem to acquire knowledge.
NN adopt various learning mechanisms (Supervised and
Unsupervised are very popular)
Dr. Sandeep Kumar Poonia SOFT COMPUTING(MTCSCS302) 22/25

23. Syllabus
Books
Introduction to SC
Fuzzy Logic
Neural Networks
Probabilistic Reasoning
Introduction to Neural Networks
Neural Networks..
NN Characteristics
Characteristics, such as:
Mapping capabilities or Pattern recognition.
Data classification.
Generalization.
High speed information processing and Parallel Distributed
Processing.
In a biological system,
learning involves adjustments to the synaptic connections
between neurons.
Architecture:
Feed Forward (Single layer and Multi layer)
Recurrent.
Dr. Sandeep Kumar Poonia SOFT COMPUTING(MTCSCS302) 23/25

24. Syllabus
Books
Introduction to SC
Fuzzy Logic
Neural Networks
Probabilistic Reasoning
Probabilistic Reasoning
Genetic algorithms
As fuzzy set theory, the probability theory deals with the
uncertainty, but usually the type of uncertainty is different.
Stochastic uncertainty deals with the uncertainty toward the
occurrence of certain event and this uncertainty is quantified by
a degree of probability. Probability statements can be combined
with other statements using stochastic methods. Most known is
the Bayesian calculus of conditional probability.
Probabilistic reasoning includes genetic algorithms, belief
networks, chaotic systems and parts of learning theory
Dr. Sandeep Kumar Poonia SOFT COMPUTING(MTCSCS302) 24/25

25. Syllabus
Books
Introduction to SC
Fuzzy Logic
Neural Networks
Probabilistic Reasoning
Probabilistic Reasoning
Genetic algorithms
Genetic algorithms optimize a given function by means of a
random search. They are best suited for optimization and tuning
problems in the cases where no prior information is available. As
an optimization method genetic algorithms are much more
effective than a random search.
They create a child generation from parent generation according
to a set of rules that mimic the genetic reproduction in biology.
Randomness plays an important role, since
the parents are selected randomly, but the best parents
have greater probability of being selected than the others
the number of genes to be muted is selected randomly
all bits in new child string can be flipped with a small
probability
Dr. Sandeep Kumar Poonia SOFT COMPUTING(MTCSCS302) 25/25