BUILDING A NEURAL NETWORK CLASSIFIER TO REDUCE THE RISK OF ACCIDENTS IN THE OPERATION OF COMPLEX TECHNICAL SYSTEMS
The article deals with the choice of mathematical apparatus for the assessment and classification of the current state of the technical system for a given set of characteristics, which was chosen as the pattern recognition method based on artificial neural network. Developed modified learning algorithm of the classifier near-miss incidents based on the method of back propagation, which differs from the classical one by the presence of the procedure of finding the global minimum of the error function. This addition improves the quality of teaching, directed on increase of efficiency of functioning of the classical algorithm. The General structure of the proposed neural network classifier the pre-emergency situations, the possibility and expediency of solving the recognition task for each pre-emergency situation separately, which made the decomposition of the problem of constructing neural network classifier. The structure of the neural network classifier consisting of one hidden layer with number of neurons equal to the number of inputs of the classifier. The task described in this article is to ensure accident-free engineering of complex technical systems, will be a further development of the subsystem "Operational management and monitoring of complex technical systems". In this case, "Neural network analyzer anomalies" will be used on every cycle where there may be problems with highly qualified service personnel The task of design, implementation, configuration and subsequent operation of the "Neural analyzer anomalies" as part of the corporate information system will also optimize the network cycles of building, deployment and operation of complex structural and functional systems, but also will accelerate the adoption of solutions for detecting faults, estimating the size of their residual life and delivery of components and parts necessary for the information system downtime to a minimum.