A FORWARD PROPAGATION LEARNING ALGORITHM OF MULTILAYERED NEURAL NETWORKS WITH FEEDBACK CONNECTIONS
多层反馈神经网络的FP学习和综合算法

A forward propagation learning algorithm(FP) of multilayered neural networks with feedback connections is presented in this paper. And the properties of cluster networks are discussed. A cluster with different grain sizes can be obtained by applying FP to cluster. Its convergent speed is just a linear function of sample size. Its computational complexity is a bilinear function of simple size and the dimension of imput vectors. The network constructed by the algorithm uses a comparatively fewer number of elements and its weight simply has one of three values, i.e., -1, 0, 1. Thus, it can be easily implemented into electronic circuits. The authors also discuss the properties of the network and show it is an ideal pattern classifier.