Classification of Images in Decision Making in the Central Nervous System of SEMS

Abstract

Problem statement: one of the important problems in modern intelligent robots, equipped with the Central nervous system (CNS), is the problem of forming images based on the analysis of sensory data. To solve this problem, it is necessary to build a classification model, on the basis of which, by logical analysis of the found regularities, the objects considered in the CNS can be attributed to a class. Purpose of research: mathematical formulation of the problem of forming images in the Central nervous system robot, analysis of the decisive rules for assigning images to a particular class of images and the construction of simple logical-probabilistic and logical-linguistic classification algorithms. Results: the mathematical formulation of the problem of forming images in the Central nervous system robot is formulated. In this problem statement, a set of images in the form of ordered sets with elements in the form of logical variables that take the value 0 or 1 is formed based on logical and mathematical analysis of sensory data about the robot’s selection environment. Each element of such sets is characterized by a set of features (attributes), whose values can be numeric, logical, or symbolic. Then these sets are compared in pairs with similar sets of reference images stored in the CNS database, and using the decision rules, the presented images are assigned to a particular class of images. Algorithms for constructing decision rules are analyzed and logical-probabilistic and logical-linguistic algorithms for implementing decision rules for image classification are obtained. Practical significance: The proposed principles of image formation in the CNS robot as a result of the analysis of sensory data characterized by a set of attributes with a certain degree of confidence, which can be set in the form of probability values or membership functions, and the resulting logical-probabilistic and logical-linguistic algorithms for their classification can be used in intelligent robots when processing incomplete and not completely reliable information.