基于算法树的武器和军事装备样品状态评估方法

V. Dudnyk, O. Grishchyn, V. Netrebko, R. Prus, M. Voloshcuk
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引用次数: 0

摘要

一种基于固定初始信息(以训练样本的形式)的分类树合成的有效机制,用于识别武器和军事装备样本的技术状况。所构建的算法分类树(模型)将准确地对整个训练样本(情景对象)进行分类(识别),根据该分类方案构建分类方案。并且具有最小的结构(结构复杂性)并由组件(模块)组成-用于分类和识别的自主算法作为结构的顶点(树的属性)。构建算法树(分类方案)模型的开发方法允许您使用大量不同类型信息(离散类型)的训练样本。在生成最终分类方案的过程中提供高精度、快速和经济的硬件资源,构建具有预定精度的分类树(模型)。提出了在已知算法(方案)和方法库(集)的基础上合成新的识别(分类)算法的方法。基于提出的算法分类树概念,建立了一组模型,对样本的技术状况进行了有效的分类和预测。本文提出了一组通用指标(参数),它可以有效地呈现分类树模型的一般特征,可以利用它从一组基于随机分类树的方法中选择最优的算法树。实际测试验证了算法树数学软件和模型的有效性。
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 assess the condition of samples of weapons and military equipment on the basis of algorithmic trees
An effective mechanism for the synthesis of classification trees based on fixed initial information (in the form of a training sample) for the task of recognizing the technical condition of samples of weapons and military equipment. The constructed algorithmic classification tree (model) will unmistakably classify (recognize) the entire training sample (situational objects) according to which the classification scheme is constructed. And have a minimal structure (structural complexity) and consist of components (modules) - autonomous algorithms for classification and recognition as vertices of the structure (attributes of the tree). The developed method of building models of algorithm trees (classification schemes) allows you to work with training samples of a large amount of different types of information (discrete type). Provides high accuracy, speed and economy of hardware resources in the process of generating the final classification scheme, build classification trees (models) with a predetermined accuracy. The approach of synthesis of new algorithms of recognition (classification) on the basis of library (set) of already known algorithms (schemes) and methods is offered. Based on the proposed concept of algorithmic classification trees, a set of models was built, which provided effective classification and prediction of the technical condition of samples. The paper proposes a set of general indicators (parameters), which allows to effectively present the general characteristics of the classification tree model, it is possible to use it to select the most optimal tree of algorithms from a set based on methods of random classification trees. Practical tests have confirmed the efficiency of mathematical software and models of algorithm trees.
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