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In this work, the sensorless speed control of a brushless direct current motor utilizing a neural network is presented. This control is done using a two-layer neural network that uses the backpropagation algorithm for training. The values provided by a Proportional, Integral, and Derivative (PID) control to this type of motor are used to train the network. From this PID control, the velocity values and their corresponding signal control (u) are recovered for different values of load pairs. Five different values of load pairs were used to consider the entire working range of the motor to be controlled. After carrying out the training, it was observed that the proposed network could hold constant load pairs, as well as variables. Several tests were carried out at the simulation level, which showed that control based on neural networks is robust. Finally, it is worth mentioning that this control strategy can be realized without the need for a speed sensor.

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In this work we used the Kinect® sensor in order to obtain tridimensional information to perform hand pose recognition. This recognition was used to implement a system that identifies all the hand poses of the Mexican Sign Language (MSL) alphabet. We used the fusion information that provides the IR and RGB cameras in order to determinate the finger's positions and assign a skeleton to the 3D data that belongs to the hands. We take into account the distances between a reference point and the phalanges as feature to distinguish among the symbols of the MSL. In order to perform hand pose recognition with the system, a three-layer neural network with backpropagation learning was implemented. The system was tested in real time with a user different from the one used to train the system, obtaining a recognition ratio of 90.27%.