Reasoning by SVD and Morphotronic Network

• Autorzy: Chaczko Z. , Resconi G.
• Języki publikacji: EN

Abstrakty

EN

The immune system of the vertebrates possess the capabilities of “intelligent” information processing, which include memory, the ability to learn, to recognize, and to make decisions with respect to unknown situations. The mathematical formalization of these capabilities forms the basis of immune-computing (IC) as a new computing approach that replicates the principles of information processing by proteins and immune networks. This IC approach looks rather constructive as a basis for a new kind of computing. With the Morphotronic System or the analogous SVD we can create effective learning process and create immune memory by the projection operators. Given the immune memory is possible to recognize and compare antigen in a way to take defense action to eliminate the dangerous cell.

Słowa kluczowe

EN

immune system; vertebrates; immune computing; morphotronic system; immune memory; antigen; dangerous cell

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2011
• Tom: Vol. 57, No. 2
• Strony: 145--152
• Opis fizyczny: Bibliogr. 9 poz., wykr.

Twórcy

autor

Chaczko Z.

autor

Resconi G.

• University of Technology, Sydney, Broadway NSW 2007, Australia,

Bibliografia

• [1] F. Deprettere (Eds), SVD and signal processing: algorithms, applications, and architectures. Institute of Electrical and Electronics Engineers: European Association for Signal Processing, 1988.
• [2] G. Resconi and Z. Chaczko, “Morphotronic Systems Theory,” in Proceedings of Computer Aided Systems Theory, Eurocast’09, LNCS, Las Palmas, Spain, 2009.
• [3] L. M. Surhone, M. T. Timpledon, and S. F. Marseken, Singular Value Decomposition: Linear Algebra, Matrix Decomposition, Signal Processing, Statistics, Moore-Penrose Pseudoinverse, Least Squares, Diagonal Matrix, Unitary Matrix, Spectral Theorem. BetaScript Publishers, 2009.
• [4] Z. Chaczko, “Towards epistemic autonomy in adaptive biomimetic middleware for cooperative sensornets,” Ph.D. dissertation, University of Technology, Sydney (UTS), Australia, July 2009.
• [5] Z. Chaczko and G. Resconi, “Morphotronic Systems Applications,” in Proceedings of Computer Aided Systems Theory, Eurocast’09, LNCS, Las Palmas, Spain, 2009.
• [6] Z. Chaczko and G. Resconi, “Organising Software Infrastructures: EgoMorphic BIM Model,” International Journal of Computing Anticipatory Systems, Chaos, vol. 21, pp. 372–385, 2008, publ. by Chaos.
• [7] A. O. Tarakanov, V. A. Skormin, and S. S. Sokolova, Immunocomputing: Principles & Applications. Springer Verlag, 2003.
• [8] G. Resconi and A. J. van der Wal, Morphogenetic Neural Network, ser. Series Studies in Fuzziness and Soft Computing. Springer-Verlag, July 2007, ch. M. Nikravesh, L. A. Zadeh and J. Kacprzyk, Forging the new Frontieres: Fuzzy Pioneers I.
• [9] G. Resconi, “The Morphogenetic Systems in Risk Analysis,” in Proceeding of the International Conference on Risk Analysis and Crisis Response, Shangchai, China, 25-26 September 2007, pp. 161–165.