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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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