A Solar - Powered charger with Neural Network Implemented on FPGAJournal: International Journal for Scientific Research and Development | IJSRD (Vol.3, No. 11)
Publication Date: 2016-02-01
Authors : Mayuri Vasantrao Patil; Prof.A.P.Hatkar;
Page : 217-219
Keywords : Powered Charger with Neural Network; FPGA;
Neural network may be a branch of the final field of intelligent management that is predicated on the conception of computer science. Neural controller will operate at totally different completely different} conditions of load current at different orbital periods with none standardization such just in case of pelvic inflammatory disease controller. During this a lift convertor is employed. The controlled boost convertor is employed as Associate in tending interface between electrical phenomenon (PV) panels and also the hundreds connected to them. It converts any input voltage among its operational vary into a continuing output voltage that's appropriate for load feeding. Associate in Nursing ANN is trained employing a back propagation with LevenbergÃ¢â?¬â?œMarquardt formula. Neural network controller design offers satisfactory result with little variety of nerve cells, thence battery in terms of memory and time square measure needed for neural network controller implementation. To implement the neural network into hardware style, it's needed to translate generated mode into device structure. VHDL language is employed to explain those networks into hardware. Satellite earth stations that settled in remote square measure as are the foremost necessary application of renewable energy. The results indicate that the planned management unit exploitation ANN is with success used for dominant the satellite earth station grid. With low exactitude artificial neural network style, FPGAs have smaller size and better speed for real time application than the DSP and VLSI chips the most effective validation performance is obtained for minimum mean sq. error. The regression between the network output and also the corresponding target is adequate to ninety nine which implies a high accuracy. The result shows that smart agreement between MATLAB.
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