Optimization of algorithms for the synthesis of controllable systems

A generalized probabilistic-structural model and a strategy for identifying the technical state of the system based on the parameters measurement results at specially organized removal points of diagnostic information (control points) are considered. An information measure of the defect localization depth (diagnosis) is proposed which specifies the ratio of information quantity, representing the comprehensive results of the diagnostic experiment, to the information quantity, characterizing the general state of the system. On account of the information criterion, two algorithms for localization of defects in technical systems and technological processes are put forward: an unconditional algorithm, in which testing is performed on a preliminarily selected set of control points, and a conditional algorithm, which implies the choice of each control point depending on the test results from the previous one. The suggested algorithms determine the sequence of control points that ensures the maximum localization depth of the defects and, thereby, facilitating the adaptability of systems to diagnosis, namely, their controllability. In addition, statistical modeling of block failures is assessed, drawing on their priori probabilities, which allows to estimate the amount of information that the test result delivers. The outlined algorithms are stochastic which makes it possible to diagnose complex systems under the conditions of a priori uncertainty, incommensurability of resource (time, performance, memory) and the volume of the problem being solved. Further development of the findings, obtained with regard to accelerated computing and under the conditions of fuzzy information, requires the use of modern information technologies, in particular, neuro-fuzzy modeling.

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