关键词: 融合神经网络, VLBP算法, 水电仿真, 信息融合, 温度模型

Abstract: A temperature model, as a nonlinear dynamic one, was set up on a basis of fused neural network for the hydroelectric plants. The VLBP (Variable Learning-rate Back Propagation) algorithm was utilized to update network gradients and weight values with the aim of eliminating the slowness in the drill application of the neural network which is easy to get into local extremum. In applications of the model, several temperature-acquisition parameters were got for sensors, then use such parameters to drill off-line the fused neural network. Thus, the real-time on-line forecasts will be available to the temperature parameters of hydroelectric power generator if using the drilled network. Actual accuracy of the model can be up to 96.5%. So, the model can be regarded as meeting the requirements of the simulation.