2024-09-242024-09-242023-02-150960-0779https://doi.org/10.1016/j.chaos.2023.113226https://hdl.handle.net/11452/45136In this study, an artificial neural network (ANN) model is generated, which is used to estimate the output pa-rameters of soliton waves produced as a result of nonlinear transmission lines (NLTLs). Three different output parameters are acquired as a consequence of the experiments carried out utilizing the five various input pa-rameters that are set in the ANN-based study. Input parameters for NLTL designs with 116 different experiments; inductor (L), input voltage (Vi) value, number of nodes (n), capacitance (C(V)) and load resistance (RLoad) values. Output parameters values, which are maximum voltage (Vmax), center frequency (fcenter), and voltage modulation depth (VMD). Input and output data; 70 % is set aside for training, 15 % for validation and the remaining 15 % for testing. Training, validation, and testing steps are repeated for the output parameters, in which case more than 99 % correlation is found as a result of each operation. An absolute percentage error value is found for each output parameter. Moreover, Mean absolute percentage error (MAPE) is calculated for these output datasets. The data set is tested for the experimental studies carried out in the literature, and it is observed that there is a compliance of over 99 % for this situation.eninfo:eu-repo/semantics/closedAccessSimplest equationGenerationArtificial neural network(ann)Electrical solitonMicrowave soliton oscillatorNonlinear transmission lines(nltls)SolitonScience & technologyPhysical sciencesMathematics, interdisciplinary applicationsPhysics, multidisciplinaryPhysics, mathematicalMathematicsPhysicsArticle00099825130000116910.1016/j.chaos.2023.113226