Analysis of the results of using logical methods and neural networks in diagnostic tasks

Abstract

The article analyzes the results of the work of neural networks and logical algorithms in the tasks of pattern recognition. Approaches to the creation of correctors that improve the results of neural network solutions are discussed. It is assumed that the data is presented in the form of objects and their characteristics, and various neural network methods can be used to process these data. However, if you examine the data by logical methods, then you can say that. neural networks provide some of the possible solutions. The solutions themselves are shown as lines of a logical function that describes the dependence of objects and their characteristics. Therefore, it is proposed to analyze and supplement these solutions. To do this, the article considers the possibility of constructing a corrector capable of constructing a logical function based on the structure of a neural network, and then implementing it in the form of logical neural networks. The construction of the corrector is carried out in two stages. At the first stage, a classifier is built in the form of disjunctions of possible solutions, and the classifier can be built only on the basis of the structure of the neural network, the data may be unknown. The second one proposes the implementation of this classifier in the form of a logical neural network. The use of such a hybrid approach can significantly affect the quality of solutions.