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This paper deals with the implementation of an Artificial Neural Network on reconfigurable hardware. The authors developed an application for character recognition using a feedforward backpropagation neural network, developed on an FPGA. In order to carry out such implementation the computational model of the neural network is translated into a FPGA synthesizable model using the Matlab/Simulink environment as well as the System Generator Xilinx tool. The results are illustrated in a prototype developed on an Atlys board. The authors focus the analysis and discussion on the main aspects considered to synthetize an ANN on an FPGA as well as the methodology to follow during such implementation, which are the main contributions of this paper. [ABSTRACT FROM AUTHOR]

In order to capture the maximum energy from the wind, control systems operating always at an optimum power has been utilized. This control system, considered as a whole, must be able of respond with an adequate precision and speed in response to the randomness and variability of the wind. The relationship between the wind speed, the blade pitch and the generator speed in order to produce the maximum power and also be able to limit the output power for large wind speeds is a very complicated one and it is very difficult to find its mathematical function. In this paper, the authors, utilizing the MATLAB SIMULINK toolboxes, propose representing this functional relation by means of an Artificial Neural Network (ANN). The parameters and characteristics of an existing wind turbine generator are utilized and it is demonstrated that it is possible to use an ANN in the simulation and control of a variable speed, variable pitch wind turbine that capture the maximum power from the wind. [ABSTRACT FROM AUTHOR]