Adaptive algorithm of learning rate for feedforward neural network

The learning rate is an important parameter for the learning process of a neural network (NN) which influents the stability and quickness of the NN. An adaptive algorithm of learning rate was proposed which satisfied the real-time requirement of the NN. The stability of the NN with such learning rate was proved in Lyapunov stability sense. By adding an amending part to the output of the NN to compensate, the influence of many unknown factors on the learning error, the method to adapt the learning rate was constructed, which could make the learning error converge quickly and stably. Simulation results show the efficiency of the algorithm.