Solid State Phenomena Vols. 97-98

Abstract: Self-formation concept as a generalization of the huge number of technologies in microelectronics was defined. Self-formation as irreversible evolution, causing self-increasing of an object complexity, is presented. Differential equation method allows description of evolution of any figures contour. Numerical model of self-formation in essence is a cellular automata of the second kind. Neither analytical nor numerical models did not involve causes of contour evolution. However causes of evolution are hidden in interactions of parameters which approximate an object and ambient materials. On the basis of above-mentioned factors, the right-dimensional topological space was created. It is the Cartesian product of the eight sets, including three Euclidean space axes, four parameter axes (defining parameters of the Euclidean points and interaction matrix) and time axis. Self-formation is a result of non-homogeneous mapping sequence in time. On the other hand non-homeomorpheous mapping indicates irreversibility of an evolution. Evolution is irreversible in time if only the object either contains the peculiar points or they arise under evolution. Therefore an interaction, defining the figure evolution out-side, does not return the object to initial state after its diversion inside and can implicate the complexity increasing. The new self-formation technologies for electron devices and integrated circuits manufacturing were carried out. Topological approach allows analysis and synthesis of real world structures, known in the areas of microelectronics, nanotechnology, photovoltaics and fuel cell technology, possibly in living world (genes, cells, organs, organism) as well. Problems remaining to be investigated are presented.

Abstract: The unreduced, universally nonperturbative analysis of arbitrary many-body interaction process reveals the irreducible, purely dynamic source of randomness. It leads to the universal definition of real system complexity, where the internally chaotic self-organisation emerges as a characteristic case of complex interaction dynamics. One obtains the causally complete description of the world structure emergence, from elementary particles to consciousness, including practically important and fundamentally substantiated propositions for self-formation research and applications.

Abstract: Autocatalytic chemical reactions may lead to spatio-temporal patterns if processed under non-equilibrium conditions. The patterns disappear when the conditions change and information stored in these non-equilibrium structures is lost since precise reconstructions are impossible. Replication of molecules, in particular of polynucleotides RNA or DNA, is an autocatalytic process too. The storage of information in polynucleotide sequences, however, allows for reconstruction of the molecules under suitable conditions. Conservation of information in polymer sequences constitutes the basic difference between chemical and biological self-organization. Evolution of RNA molecules is considered as pattern formation in sequence space, which manifests itself as another pattern in the space of minimum-free-energy structures. In addition, optimization of RNA structures and properties is visualized as an evolutionary trial-and-error process. This process can be interpreted as a simple form of learning at the level of ensembles or populations of molecules. Evolutionary optimization of RNA molecules occurs in steps: Short adaptive periods are interrupted by long epochs of quasi-stationarity during which the mean replication rate of the populations is essentially constant. Understanding of evolution is largely facilitated through consideration of sequence-structure relation as a many-to-one or non-invertible mapping from sequence space into structure space. Neutrality of sequences with respect to structure formation is highly relevant for evolutionary optimization on rugged fitness landscapes.

Abstract: In this contribution we consider constructive dynamical systems, taking one particular Artificial Chemistry as an example. We argue that constructive dynamical systems are in fact widespread in combinatorial spaces of Artificial Chemistries.

Abstract: Artificial planar systems self-formation process supported by pattern recognition theory and methods are discussed. Concept possibilities to apply pattern recognition power for improving and control self-formation processes are presented.

Abstract: The goal of this study was to analyze the possibilities of fuzzy neural networks and evolutionary programming methods for creating the human skill based stock trading systems. In stock exchange markets, the relationships between market variables are generally too complex to make rightful trading decisions and to earn stabile profits using classical system theory approach. On the other hand, there are a lot of trading experts-practicians that successfully trade stocks and achieve good results in the stock exchange markets. A useful technique for expert-knowledge extraction is the supervised learning methods, where human-experts actions are mapped using fuzzy-neural networks. In this paper we outline this procedure. Also we discuss the possibilities for improvement the proposed human skill based stock trading systems. An efficient biological system evolves slowly over the course of hundreds and housands of generations of individuals. Later generations have more fit and are more capable than earlier ones. Similarly, we have used evolutionary techniques to .evolve. the fuzzy-neural network based stock trading system, which is capable to solve the stock trading task more efficiently. Proposed procedure was tested using virtual trading system that uses historical data from US stock markets. The first results confirmed the good opportunities of the proposed approach.

Abstract: We introduce the stochastic multiplicative model of time intervals between the events, defining a multiplicative point process and analyze the statistical properties of the signal. Such a model system exhibits power-law spectral density S(f)~1/fβ, scaled as power of frequency for various values of β between 0.5 and 2. We derive explicit expressions for the power spectrum and other statistics and analyze the model system numerically. The specific interest of our analysis is related with the theoretical modeling of the nonlinear complex systems exhibiting fractal behavior and self-organization.