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For this purpose, a qualified virtual research simulator was used to conduct different types of flight tests and to collect engine data under a wide range of operating conditions. The collected data were then used to create a comprehensive database for the training of the ANN model. This process was performed using the Bayesian regularization algorithm available in the MATLAB Neural Networks Toolbox, followed by a study to identify the optimal network architecture, namely, the number of layers and the number of neurons. The validation of the methodology was accomplished by comparing the model predictions with a set of flight data collected with the flight simulator for different flight conditions and flight regimes including takeoff, climb, cruise, and descent. The results showed that the model was able to predict the engine performance in terms of fan speed, core speed, inlet turbine temperature, net thrust, and fuel flow with less than 5% relative error.<\/jats:p>","DOI":"10.2514\/1.i011220","type":"journal-article","created":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T05:52:56Z","timestamp":1695707576000},"page":"831-848","update-policy":"https:\/\/doi.org\/10.2514\/aiaa_crossmarkpolicy","source":"Crossref","is-referenced-by-count":1,"title":["Performance Model Identification of the General Electric CF34-8C5B1 Turbofan Using Neural Networks"],"prefix":"10.2514","volume":"20","author":[{"given":"Rojo Princy","family":"Andrianantara","sequence":"first","affiliation":[{"name":"University of Quebec"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Georges","family":"Ghazi","sequence":"additional","affiliation":[{"name":"University of Quebec"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0911-6646","authenticated-orcid":false,"given":"Ruxandra Mihaela","family":"Botez","sequence":"additional","affiliation":[{"name":"University of Quebec"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1387","reference":[{"key":"r2","doi-asserted-by":"publisher","DOI":"10.2514\/6.2004-4913"},{"key":"r3","doi-asserted-by":"publisher","DOI":"10.2514\/1.52746"},{"key":"r5","doi-asserted-by":"publisher","DOI":"10.3390\/app11052087"},{"key":"r6","unstructured":"BurdeaG. 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