S.S. Antsyferov - Dr.Sc. (Eng.), Professor, MGTU MIREA. E-mail: Antsyfer@yandex.ru

In technical terms cognitive technologies is the technology of construction of intellectual systems (IS), functioning according to the type of the human nervous system and possessing its guests, and in some respects - exceeding in the organization of complex behavior in solving intellectual problems. From a practical point of view seems to be important to establish some standard nomenclature of indicators of quality (parameters), adequately characterizing the principal features of the building, functioning and development IS, as well as standard methods (techniques) assessment of these indicators. These questions are currently in the initial stage of theoretical development, but as the saturation of the market IS their importance will only grow. The basic indicators of the quality of IS may act integrity, hierarchy, self-organization, adaptability, stability, processing speed and reliability. The standard of comparison in evaluating the performance of the quality of IS serves as the brain of the person or its mathematical model. The most common model is the Hopfield neural network. The main task for Hopfield model is the creation of systems possessing associative memory. However, there is a wide class of problems, for solution of which it is desirable that the system had a certain similarity of intuition and was able to learn from their mistakes. One of such tasks associated with the definition of the function values at a given point in space at a known set of values at other points. Difficulties related to the fact that the space can be very high dimension, which is typical for the problems of diagnostics of complex diseases, while a compact region this space corresponding to certain states (diagnoses), can be rather complicated geometry, in particular, not be convex. Another challenge is to forecast the values of some size on a number of its previously measured values. Both of these problems are reduced to the problem of interpolation. Neural networks are quite satisfactory to solve interpolation tasks, on their basis it is possible to create recognize, diagnostic and prediction systems. Especially the great potential of three-layer networks. The main question for these networks is associated with the creation of effective learning algorithms. Neural networks can be used as a tool for the simulation of different nonlinear systems. They are a kind of prototype of informational complexity typical for a number of physical, biological and technical systems simulation model of the investigated process. Use of the achievements of nanotechnology and bio-cybernetics in IS of neurological type will, apparently, in the nearest future much closer to indicators of quality of natural intellect.