由声明给出的部分定义泛函神经网络解的若干方法

IF 0.5 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS

AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS Pub Date : 2025-06-16 DOI:10.3103/S0005105525700542

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

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引用次数: 0

摘要

本文考虑了一种求解泛函神经网络（FN-networks）形式的算法的特征，其中初始问题的知识库和描述包含由具有规则结构的部分定义的fn -网络定义的概念，这些概念以相同类型的对象和相同结构的片段的集合的枚举形式定义，当它们的数量是未知的并且可能是无限的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Some Methods for Finding a Solution in Partially Defined Functional Neural Networks Given by Declarations

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Some Methods for Finding a Solution in Partially Defined Functional Neural Networks Given by Declarations

The paper considers the features of an algorithm for finding a solution in the formalism of functional neural networks (FN-networks), where the knowledge base and description of the initial problem contain concepts that are defined by partially defined FN-networks with a regular structure, in the form of enumerations of sets of the same type of objects and fragments of the same structure, when their number is unknown and possibly infinite.

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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.