基于经典神经网络激活函数的自适应系统中的延迟概率

IF 5 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Egyptian Informatics Journal Pub Date : 2024-10-14 DOI:10.1016/j.eij.2024.100555

Maja Lutovac Banduka , Vladimir Mladenović , Danijela Milosević , Vladimir Orlić , Asutosh Kar

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引用次数: 0

摘要

针对非线性系统设计提出了许多改进算法。目前还没有一种算法能提供与系统参数有关的闭式系统响应关系。在本文中，我们阐述了一种适用于离散时间数字非线性系统的独特方法。它能让我们更好地了解所分析的系统、算法和过程。主要贡献在于时域的闭式符号响应和对已实施算法的修改。此外，还对自适应系统和神经网络进行了比较。对于没有深厚数学知识的工程师或研究人员来说，非线性系统的设计和分析得到了更清晰的简化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Delay probability in adaptive systems based on activation function of classical neural networks

Many improved algorithms have been proposed for nonlinear system designs. There is no single procedure for providing an algorithm with closed-form system response relations as a function of system parameters. In this paper, we illustrate a unique method for discrete-time digital nonlinear systems. Provides better insight into the analyzed system, algorithm, and processes. The main contribution is closed-form symbolic responses in the time domain and modifications of the implemented algorithm. A comparison of adaptive systems and neural networks is also presented. The design and analysis of nonlinear systems are more clearly simplified for either engineers or researchers without deep mathematical knowledge.

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来源期刊

Egyptian Informatics Journal Decision Sciences-Management Science and Operations Research

CiteScore

11.10

自引率

1.90%

发文量

审稿时长

110 days

期刊介绍： The Egyptian Informatics Journal is published by the Faculty of Computers and Artificial Intelligence, Cairo University. This Journal provides a forum for the state-of-the-art research and development in the fields of computing, including computer sciences, information technologies, information systems, operations research and decision support. Innovative and not-previously-published work in subjects covered by the Journal is encouraged to be submitted, whether from academic, research or commercial sources.