基于混沌神经网络理论的机电设备状态趋势预测研究

为了对机电设备的非线性非平稳状态进行有效的趋势预测,运用混沌预测方法和混沌神经网络的预测原理,建立了基于混沌神经网络的预测模型. 以工业现场大型烟气轮机为研究对象,采用混沌神经网络和灰色预测两种方法进行了趋势预测,并对两种方法的预测结果进行了比较. 结果表明,针对烟气轮机的非线性非平稳状态,基于混沌神经网络的预测精度更高、更有效.

Electromechanical Equipment Fault Forecasting Research Based on Chaos-Neural Networks Theory

In order to predict electromechanical equipments'nonlinear and non-stationary condition effectively, the method of chaos prediction and the prediction theory based on chaos-neural networks are introduced, and the model of chaos-neural networks is set up. Aimed at the industrial smokes and gas turbine, the paper finished the prediction based on the chaos-neural networks and gray predicting method, the two prediction results are compared. The compared result shows that the prediction based on the chaos-neural networks has a higher accuracy and it can forecast the fault more effective.