关键词: 单调性, Elman神经网络, 梯度学习算法, 收敛性

Abstract: The gradient method for training Elman networks with a finite training sample set is considered.Monotonicity of the error function in the iteration is shown. Weak and strong convergence results are proved, indicating that the gradient of the error function goes to zero and the weight sequence goes to a fixed point, respectively. A numerical example is given to support the theoretical findings.

Key words: Elman network, gradient learning algorithm, convergence, monotonicity