Sharaievskyi G. Neural network software for the automatic diagnosis of nuclear power plants' components

In this work the approach to building SOM-neural networks regarding the tasks of accidental objects recognition is reviewed. Modified algorithm of studying the recognizing SOM-neural structure is proposed in condition of absence of a priori information about the power of classes' multitude to-be-recognized. In this dissertation the approach for training and automated adaptation of diagnostic in conditions of a priori uncertainty of many classes to be recognized is proposed. This approach is implemented on the basis of determination of the moment of disorder of random time series using the autoregressive model. In this work the approach for training and automated adaptation of diagnostic neuro-networking structure on the basis of Kohonen's topology in conditions of a priori uncertainty of many classes to be recognized is proposed.