Designing Algorithms for Optimization of Parameters of Functioning of Intelligent System for Radionuclide Myocardial Diagnostics

The influence of the number of complex components of Fast Fourier transformation in analyzing the polar maps of radionuclide examination of myocardium at rest and stress on the functional efficiency of the system of diagnostics of pathologies of myocardium was explored, and there were defined their optimum values in the information sense, which allows increasing the efficiency of the algorithms of forming the diagnostic decision rules by reducing the capacity of the dictionary of features of recognition.The information-extreme sequential cluster algorithms of the selection of the dictionary of features, which contains both quantitative and category features were developed and the results of their work were compared. The modificatios of the algorithms of the selection of the dictionary were suggested, which allows increasing both the search speed of the optimal in the information sense dictionary and reducing its capacity by 40 %. We managed to get the faultless by the training matrix decision rules, the accuracy of which is in the exam mode asymptotically approaches the limit.It was experimentally confirmed that the implementation of the proposed algorithm of the diagnosing system training has allowed to reduce the minimum representative volume of the training matrix from 300 to 81 vectors-implementations of the classes of recognition of the functional myocardium state

Information-extreme Machine Learning of the Control System Over the Power Unit of a Thermal Power Main Line

The study considers a method of deep machine learning of a decision-making support system of control of a power unit of a thermal power plant. We developed a method within the framework of information-extreme intelligent technology, it is based on maximization of informational capacity of a control system in the process of machine learning. We developed categorical models of information-extreme machine learning with optimization of control tolerances to recognition attributes and levels of selection of coordinates of averaged binary vector-realizations of recognition classes. We considered a modified Kullback information criterion as a criterion for optimization of learning parameters. We implemented algorithms of machine learning with polymodal and unimodal decisive rules. We formed a learning matrix based on archival data of the operation of Shostka thermal and power plant. The results of physical modeling showed that the use of polymodal decisive rules does not provide a high functional efficiency of machine learning. We ordered the alphabet of recognition classes to the magnitude of deviation of a functional state of the technological process from the standard regime for the application of unimodal decisive rules. At the same time, we constructed unimodal decisive rules according to geometric parameters of hyper-spherical containers of recognition classes х by the enclosed structure. We proved experimentally that the use of the unimodal classifier gives possibility to construct decisive rules, which error-free by a learning matrix. The obtained results give possibility to provide high functional efficiency of machine learning of control systems of technological processes whose classes of recognition intersect substantially in a space of attributes