Improved BP algorithm based on dynamically adjusting learning rate

The learning rate is usually invariable in basic Back Propagation(BP) algorithm,thus the constringency rate and stabilization of network are constrained.Therefore,a BP algorithm based on dynamically adjusting learning rate was proposed.The average absolute error between actual output value of the output layer node and the expected output value and its change ratio were regarded as independent variables,then the function relation of learning rate and two independent variables was found.According to the actual learning circumstance of network,the learning rate was adjusted dynamically.Through the instance simulation,the improved BP algorithm is of more fast constringency rate while keeping the good stabilization than basic BP algorithm.Further more,the algorithm can select the appropriate number of parameters without any condition,and it is therefore of general applicability.