Neural Networks for Identifying Civil Pilot’s Operation Sequences

Abstract

Human Error, as we all know, is inevitable during the flight process of civil aircraft. It is one of the most significant reasons for civil aircraft accidents and incidents. Therefore, to identify and avoid Human Error is becoming more and more urgent. In order to restrict the influence of Human Error, the wrong sequence of civil pilot's operation must be detected and a warning should be provided for pilot or intelligent action to correct the wrong sequence of operations. A set of effective behavior coding system is developed for expressing the pilot's operations. Pilot's operation behaviors can be quantized and operation sequences can be coded. And the set of effective pilot's behavior coding system plays an important role in reducing the probability of flight accidents caused by Human Error. For identifying whether the pilot's operation sequence is right, a database of codes of pilot's operation sequences should be built. By comparing with the codes in the database, a wrong operation sequence can be detected. Generally speaking, the database containing codes of all possible correct and wrong operation sequences is difficult to set up. As a matter of fact, the database we can develop is just a part of all possible codes of operation sequences. Therefore, those naturally correct operation sequences but not in the database may be detected as wrong ones by comparing with the correct codes in the database. This paper adopts neural networks to identify any codes of operation sequences (in database and not in database) accurately. The incomplete database is trained by neural networks to find the rule for identifying whether a specific operation sequence is correct. If the specific pilot's operation sequence disobeys the rule, a warning will be provided for pilot to rectify the operation, which reduces the probability of accidents caused by Human Error and realizes the intelligent identifying function.