摘要
针对水电仿真系统水机温度建模中存在非线性动态数学模型问题,提出了一种采用融合神经网络的温度模型·并且为消除应用中神经网络训练速度慢、容易陷入局部极值的影响,采用了可变学习速度的VLBP算法作为更新网络梯度和权值的算法·在该模型的实际应用中,首先设置多个传感器采集温度参数,然后使用采集数据对神经网络进行离线训练,而后使用训练完成的网络对水机温度参数进行实时在线预测·通过现场数据和网络预测数据的对比分析,证明该模型的实际准确率可达96 5%,可以满足实际仿真的要求·
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 Learningrate 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 temperatureacquisition parameters should be set for sensors, then use such parameters to drill offline the fused neural network. Thus, the realtime online 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% through a comparison discussed between the data acquired and forecasted using the network. So, the model can be regarded as meeting the requirements of the simulation.
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2003年第11期1037-1040,共4页
Journal of Northeastern University(Natural Science)
基金
国家自然科学基金资助项目(69873007).
关键词
融合神经网络
VLBP算法
水电仿真
信息融合
温度模型
fused neural network
variable learning-rate back propagation algorithm
hydroelectric simulation
information fusion
temperature model
