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This paper proposes a simplified model of the propulsion system of the Planar Vertical Take-Off and Landing (DAVP) with a disturbance to achieve a DAVP, based on experimental data of voltage and angular velocity to perform the estimation of forces, moments, angular velocity of rotors and current, to analyze the system power efficiency results. The DAVP experimental data are acquired with an Arduino® board at Matlab®/Simulink. The block diagram of the system and the simulation and experimental results obtained showed the effectiveness of the proposed model for estimating variables. Keywords: Propulsion system model, DAVP, Arduino, experimental data, fuzzy adaptive neurons, FAN, power efficiency

In this paper we implement neural network structure and Bayesian inference in order to improve performance black-box modeling for unknonw nonlinear systems. This kind of structure works in batch form passing both the identification and the statistical training. Two nonlinear systems and two data sets of seismic information from regions of Italy and Mexico are used to evaluate the methods. The results are satisfied.