基于神经元簇功能假设的飞行员操纵行为习惯研究

项目名称： 基于神经元簇功能假设的飞行员操纵行为习惯研究

项目编号： No.51505493

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 机械、仪表工业

项目作者： 刘嘉

作者单位： 中国人民解放军海军航空大学

项目金额： 22万元

中文摘要： 飞行员的驾驶经验和操纵习惯对飞行安全有着重要影响。更加准确的建立飞机驾驶员行为模型、更加真实的反映人类思维过程、更加有效的对操纵行为进行监控与分析，对人机系统设计、飞行训练考核以及飞行安全有着重要意义。项目拟根据神经元簇功能假设，建立带有遗忘机制的飞行员认知控制神经网络模型，反映人类的在线学习、经验体系构建和记忆遗忘等特点，通过模型仿真进一步揭示人类学习行为和经验形成机理。同时针对现有飞行考核大纲无法对行为习惯和驾驶经验等非技能因素进行考核的问题，项目拟综合考量脑力负荷和飞行技术指标，建立行为习惯和驾驶经验评判标准，更加准确的对飞行员训练水平做出评判；最后项目将运用数学模型，对飞行员操纵行为进行分析，探索快速改变行为习惯、获取驾驶经验的高效训练方法。这一研究对促进飞行安全、提高训练水平具有直接推动作用，对揭示人类学习一般规律，开展人工智能研究具有潜在应用价值。

中文关键词： 人机工程；神经网络；驾驶行为；飞行动力学；人体模型

英文摘要： The pilots' experience and the control behavior play an important role in fight safety. A proper pilot model to reflex the thinking process authentically, and a proper way to observe and analyse the pilot behavior more effectively will be of a great importance in aircraft design, fight training, rating and flight safety. In this items a kind of neural network cognition pilot model is developed based on the assumption of neural cluster. In which the human characteristics of online learning, experience system construction and memory mechanism are revealed by model simulation. As the hiding control habit, pilots' experience and other skilled factors can't be checked in the flight assessment criteria, a new method of testing is developed, in which both control ability, flight skill and mental workload are synthesized. In this valuation system the pilot's training level can be decided more accurately. Finally, the pilot behavior in landing and approaching are analysed by the pilot model, and a kind of effective training method to change the outmoded customs and construct the experience system is discussed. The research of pilot control habit in the item will play a significance role to promote the flight safety, raise the training efficiency and quality, in addition, this work has a potential value to reveal the mechanism of human learning, also the results of the item can be engaged in the artificial intelligence research.

英文关键词： ergonomics ;neural network;pilot behavior ;flight dynamics;pilot model