提高信息系统决策速度的系统方法

Yurii Artabaiev, O. Shknai, Serhii Mordvinov
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引用次数: 0

摘要

本研究的相关性在于,无论评估对象的层次结构如何,在保证给定信度的前提下,都需要提高评估对象评估过程的效率。研究的对象是信息系统。本研究的主题是评价过程的效率。本研究的假设是在给定的可靠性下提高过程的效率。在研究中,提出了一种基于生物启发算法的改进方法来提高评估过程的效率。在进行研究的过程中,利用人工智能理论的一般规定来解决智能决策支持系统中对象状态的分析问题。改进的实质是采用下列程序:—考虑到评价对象的状态的不确定性的类型;-考虑到评价对象状态的数据噪声程度;−在评价评价对象的状态时,使用蚂蚁算法和遗传算法寻找路径度量;−使用进化的人工神经网络对合成蚂蚁进行深度学习。在评估分层对象的示例中给出了使用所提出方法的示例。由于使用了额外的改进程序,指定的模型显示数据处理效率提高了15 - 22%
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Methodical approach to increase the speed of decision-making in information systems
The relevance of the research lies in the need to increase the efficiency of the process of evaluating the object of evaluation while ensuring the given reliability, regardless of the hierarchical construction of the object of evaluation. The object of research is information systems. The subject of the study is the efficiency of the evaluation process. The hypothesis of the study is to increase the efficiency of the process at a given reliability. In the study, an improved methodology for increasing the efficiency of the evaluation process based on bio-inspired algorithms was proposed. In the course of the conducted research, the general provisions of the theory of artificial intelligence were used to solve the problem of analyzing the state of objects in intelligent decision support systems. The essence of improvement is to use the following procedures: − taking into account the type of uncertainty about the state of the object of evaluation; − taking into account the degree of the noise of the data on the state of the object of evaluation; − using the ant algorithm and the genetic algorithm to find the path metric when evaluating the state of the evaluation object; − deep learning of synthetic ants using evolving artificial neural networks. An example of the use of the proposed methodology is presented in the example of the assessment of a hierarchical object. The specified model showed a 15−22 % increase in data processing efficiency due to the use of additional improved procedures
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