INTELLIGENT SIMULATION OF COMPLEX SYSTEMS USING IMMUCOMPUTING

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INTELLIGENT SIMULATION OF COMPLEX SYSTEMS USING IMMUCOMPUTING

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Title: INTELLIGENT SIMULATION OF COMPLEX SYSTEMS USING IMMUCOMPUTING

1
INTELLIGENT SIMULATION OF COMPLEX SYSTEMS USING
IMMUCOMPUTING

Svetlana P. Sokolova
Ludmilla A. Sokolova
St. Petersburg Institute for Informatics and
Automation of RAS

Kazan, 18-22 of February, 2008
2
Contents

IMMUNOCOMPUTING POSSIBILITIES
INDEX FORMAL IMMUNE NETWORK
MATHEMATICAL BASIS
APPLICATIONS OF THE IMMUNOCOMPUTING APPROACH

3
IMMUNOCOMPUTING POSSIBILITIES

Immunocomputing represents a bridge between
immunology and computer engineering,
demonstrating how quantitative advancements in
immunology can form the basis for a new computing
paradigm
Immunocomputing possibilities
capacity for memory
the ability to learn and recognize, and make
decisions in conditions of uncertainty and
incomplete information
an excellent information-processing model for
designing a powerful computing system

4
RISK INDEX
Indices reduce large quantities of variable
data (uncertainty, multidimensional and so on)
relating to a complex dynamic systems into a
single value (Data Fusion) to achieve a solution
to a practical problem Sometimes this is the
only way to represent a system and predict risks
and trends Risk Index is overall index
indicating an irregular situation
5
FORMAL IMMUNE NETWORK STRUCTURE
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TRAINING MODULE
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MODULE OF INDEX COEFFICIENTS OPTIMIZATION
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Basic Algorithm of Immunocomputing(in pseudo
code)

Learning // data mapping into FIN space
to receive a learning sample
to form learning matrix
to calculate SVD of the learning matrix
//SVDsingular value decomposition//
Recognition // data classification in FIN
to receive a situation vector //pattern
to map a vector in FIN space
to find the closest FIN point
to assign a vector the closest FIN point class

9
Monitoring Plague problem
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Plague Risk Index
11
CREDIT RISK INDEX
The analysis of credit status of the borrower -
its ability to pay off under the promissory notes
completely and in time
2. Multidimensional data
1. Interval data
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APPLICATION OF THE CREDIT RISK INDEX
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RESULT
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Conclusion

Application of Immunocomputing approach
significantly increases a potential of
realization in real systems
The considered technologies implementation to a
broader problems class, including monitoring
systems of various size and orientation

15
Thank for your attention

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