基于神经网络的非正常工况预报

分析了PTA生产中氧化反应器尾氧浓度的影响因素，提出一种用小波分析对数据进行降噪处理的方法。采用BP神经网络并对其进行了一定程度的改进。通过降噪前后的网络仿真结果对比，表明基于小波降噪的神经网络具有更好的精度和更强的泛化能力。用此网络预测尾氧浓度，实现了对非正常工况的预报。通过实际对比，表明该网络能够较理想地预报出非正常情况。

Forecast of Abnormal State Based on Artificial Neural Network

The factors that influence the reminded oxygen consistence in PTA process are introduced. In order to filter the noise of the data, a new method based on wavelet transformation theory is presented in this paper. The BP network is presented and improved. Simulation is made with or without wavelet noise filtering. The results of the two situations are compared and it indicates that the predictive precision and the generalizing ability of the neural network based on wavelet noise filtering are better. The forecast of abnormal state is made by the calculated reminded oxygen consistence. The comparison shows that the forecast of abnormal state can be made rightly.