期刊论文

中文

系统仿真学报

1004-731X

2014

26

4

892-896

10.16182/j.cnki.joss.2014.04.041

湖南省自然科学基金项目（10JJ3066）；

本校为第一机构

针对禽蛋孵化过程是一个具有高度非线性、大滞后且强耦合性的农业生产过程,常规的控制方法难以达到较好的控制效果,提出一种禽蛋孵化过程的组合预测方法。该方法分别采用神经网络模型和过程记忆神经网络对禽蛋孵化过程温、湿度进行预测,然后采用方差–协方差优选组合预测法对两种单一模型的预测结果进行加权集成,以获得较为准确的预测精度,实现禽蛋孵化过程温度和湿度的有效预测。仿真运行结果表明对两种单一预测模型的预测结果进行加权组合后得到的组合预测模型的预测精度明显要优于单一预测模型,能较好的预测孵化过程温、湿度,从而较好的保证禽蛋孵化过程的稳定控制。

Due to the incubation process which is highly nonlinear, large delay and strongly coupling and can't be controlled effectively by common method, a combination forecasting method for incubator was proposed. Firstly, the process neural networks and associative memory neural networks were respectively used on prediction for incubator, then combining the two sub-models with weighted integration by using the variance-covariance combination forecasting method, the value of the temperature and humidity of incubator could be more accurate and effective, which could make the temperature and humidity of...