Convergence of gradient method for Elman networks

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.     

Elman网络梯度学习法的收敛性

考虑有限样本集上Elman网络梯度学习法的确定性收敛性．证明了误差函数的单调递减性．给出了一个弱收敛性结果和一个强收敛结果,表明误差函数的梯度收敛于0,权值序列收敛于固定点．通过数值例子验证了理论结果的正确性．