Logical-Linguistic and Logical-Probabilistic Methods of Image Classification in Decision-Making

Abstract

The formation of images in the environment of choice and their classification is one of the important features that characterize the intelligence of modern robots. To do this, we are looking for logical patterns that can explain the available facts and predict the images being formed. Existing neural network methods require the use of pre-training on some training sample. Therefore, objects that are not included in the training sample cannot be classified. Also, the presence of contradictory examples in the training sample and a large noise level in the classified image has a significant impact on the decrease in classification accuracy. Purpose: Construction of new methods for searching for a set of logical connections inherent in the image, construction of classification models and development of structural and linguistic methods of classification of analyzed images. Methods: Logical-linguistic and logical-probabilistic classification methods are proposed, in which the decisive rule of classification is based on calculating the minimum sum of the squares of the differences in the values of the membership functions or probabilities of the elements of the attribute strings of reference and classified images. At the same time, to increase the accuracy of classification, the specified values of membership functions or probabilities can be multiplied by the coefficients of the significance of attributes. Result: The proposed classification algorithms were tested using computer simulation of classification using examples of image recognition in unmanned aerial vehicles, accident risk assessments when driving unmanned vehicles and risk assessments of project financing. The results of computer modeling showed that at a noise level of about 35% - 40%, the accuracy of image classification lay in the range of 78% - 95%. Practical significance: the research results can be used in various intelligent systems to improve the accuracy and speed of image classification