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In order to improve the training speed of multilayer feedforward neural networks, we proposed and explored fast backpropagation (BP) algorithms by introducing the hybrid global optimization conjugate gradient algorithm for the dynamic learning rate. This was to overcome the BP learning problem which caused plunging into local minima or slow convergence. Our algorithm is of a higher recognition rate than that of the Polak-Ribieve conjugate gradient and conventional BP algorithms. It showed less training time, less complication and stronger robustness than the Fletcher-Reeves conjugate gradient and conventional BP algorithms for real speech data.