Fast recursive Elman neural network modeling and learning algorithm

• Main Authors: Wang Jian, Gong Xiaoling, Wen Yanqing, Yang Guoling, Ye Zhenyun, Zhang Bingjie, Shi Xian
• Format: Patent
• Language: Chinese; English
• Published: 10.08.2016
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; PHYSICS

The invention belongs to the field of a neural network, and specifically discloses a fast recursive Elman neural network modeling and learning algorithm, which comprises a first step of selecting a model; a second step of initializing parameters; a third step of calculating an error function Ek of a training sample, turning to a fourth step if the error function value is less than an error threshold, and otherwise, calculating the gradient of the error function with respect to weights Wk and Vk; and the fourth step of testing the precision. The fast recursive Elman neural network modeling and learning algorithm provided by the invention can complete weight selection from a hidden layer to an output layer through a generalized inverse method in one step, without iteration, while optimizing an input weight matrix; and in this way, the original two layers of weights calculated using a gradient method become one during the weight update, the training speed is greatly increased, and the shortcomings that a BP algo