辨识结果表明，动态递归网络模型优于传统辨识模型，适于非线性、不确定结构的辨识。
Results of identification show that the Elman's recurrent model is superior to the traditional model. It is adaptive to the identification of the non linear and uncertain structure.
无限区间上s -分布时滞广义递归神经网络模型概周期解的全局渐近稳定性。
Global asymptotic stability of general recurrent neural network models with S-type distributed delays on infinite intervals.
本文用递归神经网络逼近非线性ARMA模型预测电力短期负荷。
The recursive neural network based nonlinear approaching ARMA model is adopted for short-term power load prediction in this paper.
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