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Artificial neural networks (ANN) has attracted attention of the academic community by the current progress that this technique has provided in speech recognition and digital media such as as image, video, audio, and signal processing. Some fields, as industrial process control and product development can be highly benefited by the development of techniques based on the proven potentialities of ANN models, allowing more accurate simulation, better adaptation to changing environments, and greater robustness in model-based fault diagnosis. Along with the advance of ANNs, there is a trend of open-source softwares use for soft computing which facilitates the access of the interested readers to implement their own codes and to explore other applications. Historically evolutionary algorithms such as the Genetic Algorithm (GA) have been implemented to evolve the architectures to search for solutions, in order to solve this fundamental issue that is still an open problem in the general case. Therefore, the present paper investigates the application of ANN to model the nonlinear aircraft turbojet engine through black-box approach. For that purpose it was used real-world measurements of aircraft engine's fuel and rotation as input and output, respectively. In order to facilitate the design, the ANN was optimized aiming to determine the best topology according to the one-step-ahead and free-run simulation. The results obtained encourage the use of automatically generated ANN architectures for dynamic system modeling.
Costa et al. (Sat,) studied this question.