This paper presents a set of concepts aiming at the reconstruction of mechanisms of economic space development. These concepts are ordered in the way that consecutive concepts add new pieces of knowledge increasing the degree of cognition of the mechanisms of the economic space. This set includes among others: a shift from one steady-state to the next steady-states, self-organization and the development far from equilibrium, multiple equilibrium, punctuated equilibrium, innovation in the phase transition, a pulsative course of the development process, an emergence of complex spatial systems, a development code of the system of regions.
The paper presents a method of developing a variable structure measurement system with intelligent components for flight vehicles. In order to find a distinguishing feature of a variable structure, a numerical criterion for selecting measuring sensors is proposed by quantifying the observability of different states of the system. Based on the Peter K. Anokhin’s theory of functional systems, a mechanism of “action acceptor” is built with intelligent components, e.g. self-organization algorithms. In this mechanism, firstly, prediction models of system states are constructed using self-organization algorithms; secondly, the predicted and measured values are compared; thirdly, an optimal structure of the measurement system is finally determined based on the results of comparison. According to the results of simulation with practical data and experiments obtained during field tests, the novel developed measurement system has the properties of high-accuracy, reliable operation and fault tolerance.
The traditional self organizing map (SOM) is learned by Kohonen learning. The main disadvantage of this approach is in epoch based learning when the radius and rate of learning are decreasing functions of epoch index. The aim of study is to demonstrate advantages of diffusive learning in single epoch learning and other cases for both traditional and anomalous diffusion models. We also discuss the differences between traditional and anomalous learning in models and in quality of obtained SOM. The anomalous diffusion model leads to less accurate SOM which is in accordance to biological assumptions of normal diffusive processes in living nervous system. But the traditional Kohonen learning has been overperformed by novel diffusive learning approaches.