A New Algorithm to Improve the Learning Performance of Neural Network Through Result-Feedback

The combination of input vector tuning with traditional weight tuning of back propagation algorithm results in a new algorithm on the basis of result feedback （FBBP） This FBBP based algorithm is an inner and outer layer learning method in which weight value renewing plays the dominating role with the assistance of input renewing It minimizes the error function of neural network through the dual functioning of weight value and input vector value tuning In the process neural network learning and training are estimated from an angle of result feedback along with the objective to effectively improve the learning performance of feed forward neural network Quite a few simulation experiments serve to make comparisons between the FBBP algorithm, the BP algorithm with momentum term, and a recently published algorithm that used weight updating method to speed up convergence Experiment results are discussed in detail The results show that the new algorithm has the dual merits of quick training speed and good generalization capability It proves to be a very effective learning method