Mr. Nickolay M. Bykov Profile

Nickolay M. Bykov

at Vinnitsa State Technical Univ

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Proceedings Article | 21 March 2003 Paper

Unified method of knowledge representation in the evolutionary artificial intelligence systems

Nickolay Bykov, Katherina Bykova

Proceedings Volume 5098, (2003) https://doi.org/10.1117/12.487940

KEYWORDS: Binary data, Evolutionary algorithms, Stereolithography, Detection and tracking algorithms, Artificial intelligence, Intelligence systems, Logic, Bismuth, 3D modeling, Fuzzy logic

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The evolution of artificial intelligence systems called by complicating of their operation topics and science perfecting has resulted in a diversification of the methods both the algorithms of knowledge representation and usage in these systems. Often by this reason it is very difficult to design the effective methods of knowledge discovering and operation for such systems. In the given activity the authors offer a method of unitized representation of the systems knowledge about objects of an external world by rank transformation of their descriptions, made in the different features spaces: deterministic, probabilistic, fuzzy and other. The proof of a sufficiency of the information about the rank configuration of the object states in the features space for decision making is presented. It is shown that the geometrical and combinatorial model of the rank configurations set introduce their by group of some system of incidence, that allows to store the information on them in a convolute kind. The method of the rank configuration description by the DRP - code (distance rank preserving code) is offered. The problems of its completeness, information capacity, noise immunity and privacy are reviewed. It is shown, that the capacity of a transmission channel for such submission of the information is more than unit, as the code words contain the information both about the object states, and about the distance ranks between them. The effective algorithm of the data clustering for the object states identification, founded on the given code usage, is described. The knowledge representation with the help of the rank configurations allows to unitize and to simplify algorithms of the decision making by fulfillment of logic operations above the DRP - code words. Examples of the proposed clustering techniques operation on the given samples set, the rank configuration of resulted clusters and its DRP-codes are presented.

Proceedings Article | 12 June 2001 Paper

Development of effective strategy of pattern recognition

Nickolay Bykov, Ivan Kuzmin, Antonina Yakovenko

Proceedings Volume 4425, (2001) https://doi.org/10.1117/12.429699

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This paper presents the basic concepts of designing of optimal consecutive-parallel strategy of speech recognition, based on the general-system efficiency criterion, which realizes the recognition procedure with alternatives pruning. It allows to increase the recognition rate on average and also to use a modular principle of designing of the recognition program via connecting additional modules while the complexity of the recognition task is increased. The task of the development of optimal recognition strategy in untrivial statement should be connected with an overall performance of the system as a whole, but not just with the condition of achievement of the given recognition accuracy. The complete formalization of the decision of this task for a general case is unknown today and is hardly possible. The formalization of the procedure of the development of the effective recognition strategy is possible only when feature sets of patterns are chosen by informal methods, and the criteria for the efficiency estimation are also chosen. Then the formal statement of the task of the development of the effective recognition strategy can be formulated as the task of the optimal decision tree searching, which is based on the general-system efficiency criterion. On each step of this hierarchical decision tree such features subset is selected, which reduces the entropy about patterns in maximum degree and increases the classification rate.

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