Air Pollutants Monitoring Data Recovery of Henhouse Based on QGSA-SVM

In order to solve the data missing problem caused by sensor faults during the air pollutants monitoring in henhouse, a method for missing data recovery was presented based on support vector machine. To obtain better recovery accruacy, SVM regression model parameters were optimized by by a novel hybrid optimization algorithm which was combined standard genetic algorithm with quantum genetic strategy and simulated annealing tactics. The estimation results of missing data shown that the maximal relative error was 5.87%; the average relative error was 1.77%. It is verified that this method of missing data recovery based on QGSA-SVM is feasible and valid.