Incremental Topology Generated for ANN: A Case Study on a Helicopter’s Flight Simulation

Abstract

This paper presents a method for the development of artificial neural networks (ANN) that consists in the use of a search space algorithm to adjust the components of an ANN’s initial structure, based on the performance obtained by different network configurations. Also, it is possible to represent an ANN’s structure as a genetic sequence, which enables directly loading a corresponding genetic sequence to instantly generate and run a previously trained ANN. This paper also shows some results obtained by different ANNs developed by this method, which demonstrate its features by analyzing its accuracy and trueness. As an example for application of this method, a case study is presented for a specific flight simulation, using data obtained from a helicopter’s flight dynamics simulator for ANN training. Helicopter flight dynamics is a relevant study, for it can be used, for example, to provide precise data to a flight simulator, which implies in an important issue for pilot training, and subsequently, this type of application may help reducing the probability of pilot’s faults in a real flight mission. Finally, some considerations are made about the work shown in this paper as the results, discussions and conclusions are presented.