Problem statement: Accurate weather forecasting plays a vital role for planning day to day activities. Neural network has been use in numerous meteorological applications including weather forecasting. Approach: A neural network model has been developed for weather forecasting, based on various factors obtained from meteorological experts. This study evaluates the performance of Radial Basis Function (RBF) with Back Propagation (BPN) neural network. The back propagation neural network and radial basis function neural network were used to test the performance in order to investigate effective forecasting technique. Results: The prediction accuracy of RBF was 88.49%.
Conclusion:The results indicate that proposed radial basis function neural network is better than back propagation neural network.
In this paper design of DC-DC boost converter with constant current control, charging is presented to charge the battery of electric vehicles. The different methods of battery charging are discussed. The charging profile of different types of batteries is investigated and compared with respect to charging time. The battery current is sensed and compared with a reference current and the generated actuating signal which is an error is feed to PI controller to compute a duty cycle of boost converter for constant current operation. A 6 V dc supply is obtained by using a step down transformer and diode rectifier. Boost converter parameters are designed for continuos conduction mode operation. The limiting values of duty cycle are fixed in the range of 0.5 to 0.6 for constant current operation. Simulation is carried out using MATLAB software for constant current operation connected to a 50 Ah, 12 V battery load. The constant current operation is achieved using negative feedback control. The switching frequency of boost converter is set to 20 kHz. The filter components are also designed to reduce ripple content within limited values. The simulation result shows the effectiveness of charging control for hardware implementation.
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