人在回路导弹控制研究:AHP灰色评估法的应用

需积分: 15 2 下载量 90 浏览量 更新于2024-09-17 收藏 855KB PDF 举报
"基于AHP灰色评估法的人在回路导弹控制研究,通过结合层次分析法(AHP)和灰色评估法,建立了一种评估模型,用于分析飞行员在导弹控制系统中的综合能力,旨在提高巡航导弹的命中精度。" 本文是关于军事科技领域的导弹控制研究,特别是探讨了人在回路导弹控制策略中如何运用AHP(Analytic Hierarchy Process)灰色评估法来提升系统效能。AHP是一种多准则决策分析工具,用于处理复杂问题,通过比较不同因素的相对重要性来建立决策层次结构。而灰色评估法则是一种处理不完全信息和不确定性的方法,尤其适用于数据不充分或模糊的环境。 作者王亚飞、方洋旺和周晓滨建立的AHP灰色评估模型,旨在评估飞行员在导弹控制任务中的综合能力。他们首先定义了评估的层次结构,包括多个层次和多个评价指标。这些指标可能包括反应速度、决策能力、空间感知、心理素质等,这些因素对导弹的精确控制至关重要。通过AHP,可以量化这些指标之间的相对权重,然后结合灰色评估法处理不确定性和不完整的数据,得出飞行员的整体能力评估。 文章阐述了使用该模型进行评估的基本步骤,包括构建评估指标体系、确定指标权重、灰色关联分析和最终的综合评估。其中,灰色关联分析用于衡量各个指标与理想状态的相似度,从而给出全面的评估结果。通过模拟实验,模型的正确性和有效性得到了验证,这为飞行员识别自身技能短板提供了理论依据。 这项研究的重要性在于,它不仅提供了一种评估飞行员能力的新方法,还为提高导弹的拦截精度提供了理论支持。在实际操作中,了解和改善飞行员的能力可以显著影响导弹系统的整体性能,特别是在高风险的防御和攻击任务中。因此,这一研究对军事科技的发展和战术策略的优化具有重要的理论参考和应用价值。 关键词涉及AHP(层次分析法)、灰色评估、人在回路以及灰类,这些都是该研究的核心概念,突显了研究的技术深度和实用性。中图分类号TJ765.2和文献标志码A表明这是一篇科技期刊论文,属于军事工程技术领域,具有较高的学术价值。
2021-09-22 上传
Human-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively. Summary Most machine learning systems that are deployed in the world today learn from human feedback. However, most machine learning courses focus almost exclusively on the algorithms, not the human-computer interaction part of the systems. This can leave a big knowledge gap for data scientists working in real-world machine learning, where data scientists spend more time on data management than on building algorithms. Human-in-the-Loop Machine Learning is a practical guide to optimizing the entire machine learning process, including techniques for annotation, active learning, transfer learning, and using machine learning to optimize every step of the process. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Machine learning applications perform better with human feedback. Keeping the right people in the loop improves the accuracy of models, reduces errors in data, lowers costs, and helps you ship models faster. About the book Human-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively. You'll find best practices on selecting sample data for human feedback, quality control for human annotations, and designing annotation interfaces. You'll learn to create training data for labeling, object detection, and semantic segmentation, sequence labeling, and more. The book starts with the basics and progresses to advanced techniques like transfer learning and self-supervision within annotation workflows. What's inside Identifying the right training and evaluation data Finding and managing people to annotate data Selecting annotation quality control strategies Designing interfaces to improve accuracy and efficiency About the author Robert (Munro) Monarch is a data scientist and engineer who has built machine learning data for companies such as