Department of Signal Theory, Communications and Telematics Engineering, University of Valladolid, 47011 Valladolid, Spain.
Sensors (Basel). 2012 Oct 17;12(10):14004-21. doi: 10.3390/s121014004.
This paper presents a system based on an Artificial Neural Network (ANN) for estimating and predicting environmental variables related to tobacco drying processes. This system has been validated with temperature and relative humidity data obtained from a real tobacco dryer with a Wireless Sensor Network (WSN). A fitting ANN was used to estimate temperature and relative humidity in different locations inside the tobacco dryer and to predict them with different time horizons. An error under 2% can be achieved when estimating temperature as a function of temperature and relative humidity in other locations. Moreover, an error around 1.5 times lower than that obtained with an interpolation method can be achieved when predicting the temperature inside the tobacco mass as a function of its present and past values with time horizons over 150 minutes. These results show that the tobacco drying process can be improved taking into account the predicted future value of the monitored variables and the estimated actual value of other variables using a fitting ANN as proposed.
本文提出了一个基于人工神经网络（ANN）的系统，用于估计和预测与烟草干燥过程相关的环境变量。该系统已经通过来自具有无线传感器网络（WSN）的真实烟草干燥器的温度和相对湿度数据进行了验证。使用拟合 ANN 来估计烟草干燥器内部不同位置的温度和相对湿度，并预测它们在不同的时间范围内。当将温度作为其他位置的温度和相对湿度的函数进行估计时，可以达到低于 2%的误差。此外，当将烟草质量的温度作为其当前和过去值的函数进行预测时，在超过 150 分钟的时间范围内，与插值方法相比，可以达到低 1.5 倍左右的误差。这些结果表明，可以通过使用拟合 ANN 来考虑所监测变量的未来预测值和其他变量的实际估计值，从而改善烟草干燥过程。
