Stochastic Resonance Synergetics: a dynamical data modeling methodology for multidimensional scaling
Dr. Milan Jovovic
Hosť Ústavu informatiky, PF UPJŠ, Košice v rámci štipendia SAIA
Data decomposition methodology, based on the computation of renormalized synergies, is proposed for analysis and coding. It is a scale-space computing approach, employing covariance differentiability and the scale-space wave information propagation. This methodology is written for multi-dimensional data scaling along a single scaling parameter, and computing with dynamical cascades results in a binary tree data decomposition. It decomposes data in the structure suitable for scalable storage, transmission and computation. We propose it within the quantum information theory that lays down a new perspective to networked systems dynamics and computation. We explore its relationship to the complexity and computability issues of statistical physics, from the perspective of information theory. Examples of dynamical data modeling in various complex systems will be shown. It proposes also our approach to the NP completeness solving, pervasive in computational problems, in life sciences especially.