Elman神经网络在水电站入库流量短期预测中的应用

Application of Elman neural network in short-term reservoir inflow forecasting in hydropower station

Elman神经网络具有适应时变特性的能力,对历史数据具有敏感性,具备自主学习的优势,能以任意精度逼近任意非线性映射。梯度下降法可使函数具有单调递减性、梯度收敛于0等特点。采用梯度下降法和Elman神经网络相结合的方法进行水电站入库流量短期预测,比传统的Back Propagation神经网络预测精度具有明显的优势。

Elman neural network has the adaptability to time-varying characteristics,the sensitivity to historical data and the advantage of self-learning,it can approximate to any nonlinear mapping by an arbitrary accuracy. Meanwhile,the gradient descent method can make function has properties of monotone decreasing and gradient converges to 0. Compared with the traditional Back Propagation neural network,the method which combining the gradient descent method and Elman neural network has better prediction precision for the short-term reservoir inflow forecasting in hydropower station.