利用部分定义的功能神经网络的常规特征求解

IF 0.5 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS

AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS Pub Date : 2024-06-05 DOI:10.3103/S0005105524700067

V. N. Betin, V. F. Ivashchenko, A. P. Suprun

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引用次数: 0

摘要

摘要在本文中，我们提出了利用功能神经网络（FN-networks）的结构特征来提高基于知识库的系统效率的技术。文章讨论了部分定义的 FN 网络的情况，这些网络具有规则的结构，被设置为具有共同结构的多个相似对象和片段的列表，其数量未知，可能是无限的。它提供了一种寻找解决方案的算法，该算法基于部分定义网络中有限的、完全确定的局部片段的动态形成，以及随后将结果转移到整个 FN 网络（可能是无穷无尽的）。

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Usage of Regular Features of Partially Defined Functional Neural Networks to Find a Solution

In this paper, we propose techniques to improve the efficiency of systems acting with knowledge bases and based on functional neural networks (FN-networks) formalism due to the use of their structural features. The article addresses the case of partially defined FN-networks, which have a regular structure and are set as lists of multiple similar objects and fragments with common structure such that their number is unknown and may be unlimited. It offers an algorithm to find solution, which is based on dynamic formation of limited, fully determined local fragments of partially defined network and subsequent transfer of the results to the entire FN-network, which may be endless.

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来源期刊

AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS COMPUTER SCIENCE, INFORMATION SYSTEMS-

自引率

40.00%

发文量

期刊介绍： Automatic Documentation and Mathematical Linguistics is an international peer reviewed journal that covers all aspects of automation of information processes and systems, as well as algorithms and methods for automatic language analysis. Emphasis is on the practical applications of new technologies and techniques for information analysis and processing.