A weight training algorithm of neural network based on iterative learning was proposed for the shortcoming of traditional BP algorithm, such as slow convergence and easily trapped into local minimal. The algorithm combined the principle of iterative learning with neural network, and it made use of the current and the previous training error to correct the neural network weights. It improved the speed of neural network training. Simulation results show the effectiveness of the algorithm.
2013, 33 (4):
83-86.