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首页> 美国政府科技报告 >Knowledge-Based Aircraft Automation: Managers Guide on the use of Artificial Intelligence for Aircraft Automation and Verification and Validation Approach for a Neural-Based Flight Controller
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Knowledge-Based Aircraft Automation: Managers Guide on the use of Artificial Intelligence for Aircraft Automation and Verification and Validation Approach for a Neural-Based Flight Controller

机译：基于知识的飞机自动化：管理人员使用人工智能进行飞机自动化和基于神经的飞行控制器的验证和验证方法指南

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The ultimate goal of this report is to integrate the powerful tools of artificial intelligence into the traditional process of software development. The future goal is to transition artificial intelligence from an emerging technology to a standard technology that is considered early in the life cycle process to develop state-of-the-art aircraft automation systems. This report addresses the future goal in two ways. First, it provides a matrix that identified typical aircraft automation applications conducive to various artificial intelligence methods. The purpose of this matrix is to provide top-level guidance to managers contemplating the possible use of artificial intelligence in the development of aircraft automation. Second, the report provides a methodology to formally evaluate neural networks as part of the traditional process of software development. The matrix is developed by organizing the discipline of artificial intelligence into six methods: logical, object representation-based, distributed, uncertainty management, temporal, and neurocomputing. Next, a study of existing aircraft automation applications that have been conducive to artificial intelligence implementation results in five categories: pilot-vehicle interface, system status and diagnosis, situation assessment, automatic flight planning, and aircraft flight control. The resulting matrix provides management guidance to understand artificial intelligence as it applied to aircraft automation. The approach taken to develop a methodology to formally evaluate neural networks as part of the software engineering life cycle is to start with the existing software quality assurance standards and to change these standards to include neural network development. The changes include evaluation tools that can be applied to neural networks at each phase of the software engineering life cycle. The result is a formal evaluation approach to increase the product quality of systems that use neural networks for their implementation.

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