Abstract:In order to solve the problem of slow convergence of traditional BP (Back Propagation) neural network, through the BP neural network build the fire point prediction model, we use an adaptive learning rate method to improve the BP neural network, by comparison, the algorithm converges faster, and the output of the model achieves the desired effect. At the same time, an improved algorithm is realized by using the dynamic reconfigurable technology of FPGA. Through the simulation and results test, the design greatly reduces the prediction time on the basis of the prediction results and provides a theoretical basis for environmental prediction and detection trajectory planning.