Chen, XinVillegas Prados, David2019-10-302019-10-302018-072018-07-12https://hdl.handle.net/10016/29092Plasma diagnostics consist on predicting the plasma parameter by measuring their properties. Models to predict these parameters are based on theoretical equations, which sometimes are complicated and tedious to solve. This thesis aims to evaluate a new method to obtain these plasma parameters using a branch of artificial intelligence, the Artificial Neural Network. To develop this thesis, the software MATLAB was used to create the neural network. Two theories were tested: Planar Langmuir Probe and Orbital Motion Limited. And two problems were solved: forward problem to obtain the properties from the parameters and the inverse problem to obtain the parameters from the properties. The later approach is used to evaluate the parameters obtained by network with experimental data from laboratory measurements. Artificial Neural Network was implemented successfully in both forward and inverse problems with incredible accuracy. The results obtained have created a theoretical framework for plasma diagnostic using neural networks and it has been concluded that they can work with any theory presented, as long as as proper database is obtained. Regarding the simulation with experimental data, the results were not good respect the measurements taken. Although the network performed well, the database used for training and simulation had discrepancies from realistic data since it was obtained from equations. Sources of error of this last problem are found and different approaches and work to be developed in the future are proposed.engAtribución-NoComercial-SinDerivadas 3.0 EspañaPlasma diagnosticsArtificial IntelligenceArtificial neural networksMatlabPlanar Langmuir ProbeOrbital Motion Limitedbachelor thesisAeronáuticaopen access