摘要：To solve the data missing problem caused by sensor faults during the air pollutants monitoring in henhouse, a method for missing data recovery was proposed based on support vector machine (SVM). Multiple factors that influence monitoring values of the air pollutants in henhouse, such as temporal, spatial and environmental, were considered to established a SVM regression model to estimate the missing data of the air pollutants monitoring. Meanwhile, to obtain better prediction accuracy, regression model parameters were optimized by a novel hybrid optimization algorithm which was combined standard genetic algorithm with quantum genetic strategy and simulated annealing tactics. Taking the data processing of the ammonia (NH3) concentration as an example, the proposed method was tested with the monitoring data of 3 days in a farm. The estimation results of missing data shown that there was a litter error between the estimated data and monitoring data, the maximal relative error was 5.87% (percent), the average relative error was 1.77% (percent). It is verified that this method of missing data recovery is feasible and valid.
关键词：Genetic algorithm; quantum genetic; simulated annealing; support vector ; machine; henhouse; air pollutants monitoring; data recovery
