Bilgehan, Makineci HasanMustafa, HüsrevoğluHakan, Karabörk2022-10-082022-10-0820222631-8695https://doi.org/10.1088/2631-8695/ac7a0bhttps://hdl.handle.net/20.500.13091/2950In recent years, important research has been conducted in Machine Learning (ML), especially on Artificial Neural Networks (ANN). Adaptive-Network Based Fuzzy Inference Systems (ANFIS) and Particle Swarm Optimization-Fuzzy Inference System (PSO-FIS) algorithms are popular ML algorithms like ANN. In terms of their working architecture and results, ANN, ANFIS, and PSO-FIS algorithms can obtain useful solutions for different nonlinear problems. This study evaluated the performance of the ANN, ANFIS, and PSO-FIS algorithms and compared the estimation results. Regarding the application, the test and target data was obtained from the flights performed with Unmanned Aerial Vehicles (UAV), including how long the UAV operates (i.e., Flight Time, FT) and how much battery the UAV consumes during the flight (i.e., Battery Consumption, BC). To obtain FT and BC outputs, sixty-five pre- and post-flight data tables were created. The best iterations for estimating the outputs using the three ML algorithms (considering the minimum/maximum values, RMSE, R, and R2) were determined and discussed based on the training, validation, and test estimations. © 2022 IOP Publishing Ltd.eninfo:eu-repo/semantics/closedAccessANFISANNbattery consume estimationflight time estimationperformance evaluationPSO-FISUAVAntennasFuzzy neural networksLearning algorithmsMachine learningParticle swarm optimization (PSO)Secondary batteriesAdaptive network-based fuzzy inference systemAdaptive-network- based fuzzy inference systemsAerial vehicleBattery consume estimationFlight timeFlight time estimationFuzzy inference systemsParticle swarmParticle swarm optimization-fuzzy inference systemPerformances evaluationSwarm optimizationTime estimationUnmanned aerial vehicleFuzzy inferenceArticle10.1088/2631-8695/ac7a0b2-s2.0-85133728252