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 Learning sample “immunization” Index values given for training sample Training module «antibody» Pattern recognition module «antigen» «binding energy» Module of index coefficients optimization Calculating of index values Index coefficients

6. TRAINING MODULE Forming of training matrix Singular value decomposition Selection of vector for indices formation Selection of the most significant indices

7. MODULE OF INDEX COEFFICIENTS OPTIMIZATION Forming of index coefficients matrix Singular Value Decomposition Calculating of pseudo inverse matrix Calculating of optimal index coefficients

8. 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 //SVD–singular 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

10. Plague Risk Index

11. CREDIT RISK INDEX group1 client group2 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:

12. APPLICATION OF THE CREDIT RISK INDEX

13. RESULT

14. 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