EE04 804(B) Soft Computing Ver. 1.2

Class 1. Introduction

February – 21st,2012

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Sasidharan Sreedharan www.sasidharan.webs.com

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Syllabus

Artificial Intelligence Systems- Neural

Networks, fuzzy logic, genetic

algorithms, Artificial neural networks:

Biological neural networks, model of an

artificial neuron, Activation functions,

architectures, characteristics- learning

methods, brief history of ANN research-

Early ANN architectures (basics only)-

McCulloh & Pitts model, Perceptron,

ADALINE, MADALINE

Objective

To acquaint the students with important soft

computing methodologies – neural

networks, fuzzy logic, genetic algorithms,

and genetic programming.

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What is meant by soft computing?

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Definition: Soft computing refers to a consortium of

computational methodologies by including components

such as Fuzzy Logic, Neural Networks, Genetic

Algorithms etc in Artificial Intelligence platform to apply

the acquired information to new conditions.

Text Book: Neural Networks, Fuzzy Logic and Genetic Algorithms

Synthesis and Applications S Rajasekaran and Vijayalakshmi Pai

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What is meant by soft computing?

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Earlier computational approaches could model and

precisely analyze only relatively simple systems.

More complex systems arising in biology, medicine, the

humanities, management sciences, and similar fields

often remained intractable to conventional

mathematical and analytical methods.

That said, it should be pointed out that simplicity and

complexity of systems are relative, and many

conventional mathematical models have been both

challenging and very productive.

Soft computing deals with imprecision, uncertainty,

partial truth, and approximation to achieve

practicability, robustness and low solution cost.

Components of soft computing include:

Neural networks (NN)

Fuzzy systems (FS)

Evolutionary computation (EC), including:

Evolutionary algorithms

Harmony search

Swarm intelligence

Ideas about probability including:

Bayesian network

Chaos theory

Perceptron

• Soft computing deals with imprecision, uncertainty, partial truth, and

approximation to achieve practicability, robustness and low solution cost.

• Components of soft computing include:

Neural networks (NN)

Fuzzy systems (FS)

Evolutionary computation (EC), including:

Evolutionary algorithms

Harmony search

Swarm intelligence

Ideas about probability including:

Bayesian network

Chaos theory

Perceptron

• Soft computing techniques resemble biological processes more closely than

traditional techniques, which are largely based on formal logical systems, such as

sentential logic and predicate logic, or rely heavily on computer-aided numerical

analysis (as in finite element analysis).

• Unlike hard computing schemes, soft computing techniques exploit the given

tolerance of imprecision, partial truth, and uncertainty for a particular problem.

• Inductive reasoning plays a larger role in soft computing than in hard

computing.

Soft computing (SC)

Objective:

Mimic human reasoning

Main constituents:

Neural networks

Fuzzy systems

Evolutionary Algorithms

Genetic Algorithm

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Constituents of SC

Fuzzy systems => imprecision

Neural networks => learning

Evolutionary computing => optimization

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Over 24 000 publications today

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Soft Computing Verses Hard Computing

The term ‘soft computing’ was introduced by Lotfi A Zadeh of the university of California, Berkeley , USA

Soft Computing differs from hard computing (conventional computing) in its tolerance to imprecision, uncertainty and partial truth.

Hard computing methods are predominantly based on mathematical approaches and demand a high degree of precision and accuracy.

In engineering problems, the input parameter cannot be determined with high degree of precision.

The role model for soft computing is human mind, biological systems.

A powerful means for obtaining solutions to problems quickly. The guiding principle of soft computing is to accept the tolerance for

imprecision, uncertainty and partial truth to achieve tractability, robustness and low cost solution.

It is part of intelligent system.

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Intelligent systems

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•Intelligence: System must perform meaningful

operations.

•Interprets information.

• Comprehends the relations between the

phenomena or objects.

• Applies the acquired information to new

conditions.

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Advantages of SC

Models are based on human reasoning.

Models can be - simple - comprehensible - fast when computing - good in practice

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Integration of soft computing technologies

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Neural networks

Simplified model of biological nervous system analogous to human brain with large number of neurons.

Learns by example (Supervised learning and unsupervised learning)

Once trained , the network can be put to effective use in solving unknown or untrained instances of the problem.

Different architectures such as single layer feed and multi layer network.

Can be applied to problems in pattern recognition, image processing, data compression, forecasting, optimization etc.

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Neural networks (NN, 1940's)

Neural networks offer a powerful method to explore, classify, and identify patterns in data.

Website of Matlab

Neuron: y=wixi

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InputsN eurons

(1 layer)O utputs

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Fuzzy Logic Fuzzy set theory proposed by

Lotfi A zadeh. Generalization of classical set

theory. Fuzzy logic representations

founded on Fuzzy set theory try to capture the way humans represent and reason with real world knowledge in the face of uncertainty.

Wide applications in consumer electronics.

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Fuzzy Logic Washing Machine

Fuzzy Logic Rice Cooker

Fuzzy Logic Deal with imprecise entities in automated

environments (computer environments)

Base on fuzzy set theory.

Most applications in control and decision making

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Omron’s fuzzy processor

Matlab's Fuzzy Logic

Toolbox

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Model construction (SC/fuzzy)

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0

0,2

0,4

0,6

0,8

1

1,2

0 2 4 6 8 10 12

X

Y

If x0, then y1

If x5, then y0.5

If x10, then y0

- Approximate values

- Rules only describe typical cases (no rule for each input).

=> Small rule bases.

- A group of rules are partially fired simultaneously.

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Genetic Algorithms

Developed in 1970 by John Holland.

Random search which mimic some of the processes of natural evolution.

Based on a qualifying function termed as fitness function.(fitness means figure of merit)

Genetic operators such as reproduction, cross over , mutation etc are used.

Used for optimization applications

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SC applications: control

Heavy industry (Matsushita, Siemens,Stora-Enso)

Home appliances (Canon, Sony, Goldstar, Siemens)

Automobiles (Nissan, Mitsubishi, Daimler-Chrysler, BMW, Volkswagen)

Spacecrafts (NASA)

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SC applications: business

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•hospital stay prediction,

•TV commercial slot evaluation,

•address matching,

•fuzzy cluster analysis,

•sales prognosis for mail order

house,

•multi-criteria optimization etc.

•(source: FuzzyTech)

•supplier evaluation for

sample testing,

•customer targeting,

•sequencing,

•scheduling,

•optimizing R&D

•projects,

•knowledge-based

prognosis,

•fuzzy data analysis

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SC applications: robotics

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Fukuda’s lab

Joseph F.

Engelberger

We are proud to

announce that the

HelpMate Robotic

Courier

has been acquired by

Pyxis Corporation.

Entertainment

robot AIBO

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SC applications: others

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•Statistics

•Social sciences

•Behavioural sciences

•Biology

•Medicine

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SC and future SC and conventional methods should be used in

combination.

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References

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1. J. Bezdek & S. Pal, Fuzzy models for pattern recognition (IEEE Press, New York, 1992).

2. L. Zadeh, Fuzzy logic = Computing with words, IEEE Transactions on Fuzzy Systems, vol. 2, pp. 103-111, 1996.

3. L. Zadeh, From Computing with Numbers to Computing with Words -- From Manipulation of Measurements to

Manipulation of Perceptions, IEEE Transactions on Circuits and Systems, 45, 1999, 105-119.

4. L. Zadeh, Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic,

Fuzzy Sets and Systems 90/2 (1997) 111-127.

5. H.-J. Zimmermann, Fuzzy set theory and its applications (Kluwer, Dordrecht, 1991).

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