Liriomyza huidobrensis （Blanchard） is an important vegetable pest of pathology. In order to improve the accuracy of prediction of Liriomyza huidobrensis and to control the Liriomyza huidobrensis effectively, this pa...Liriomyza huidobrensis （Blanchard） is an important vegetable pest of pathology. In order to improve the accuracy of prediction of Liriomyza huidobrensis and to control the Liriomyza huidobrensis effectively, this paper presents a new prediction model by principal components analysis （PCA） and back propagation artificial neural network （BP-ANN） methods. The historical data from 1999 to 2007 on population occurrence are analyzed in order to find out a non-linear relationship between the pest occurrence and the meteorological factors. And then by using analysis results, the prediction model of Liriomyza huidobrensis occurrence in Jianshui in Yunnan is built. The new model has successfully applied to verify the paddy stem borer population occurrence in 2006. Test results show that the new prediction model with BP-ANN and PCA can improve the prediction accuracy.展开更多
Abstract-Common spatial pattern （CSP） algorithm is a successful tool in feature estimate of brain-computer interface （BCI）. However, CSP is sensitive to outlier and may result in poor outcomes since it is based on...Abstract-Common spatial pattern （CSP） algorithm is a successful tool in feature estimate of brain-computer interface （BCI）. However, CSP is sensitive to outlier and may result in poor outcomes since it is based on pooling the covariance matrices of trials. In this paper, we propose a simple yet effective approach, named common spatial pattern ensemble （CSPE） classifier, to improve CSP performance. Through division of recording channels, multiple CSP filters are constructed. By projection, log-operation, and subtraction on the original signal, an ensemble classifier, majority voting, is achieved and outlier contaminations are alleviated. Experiment results demonstrate that the proposed CSPE classifier is robust to various artifacts and can achieve an average accuracy of 83.02%.展开更多
基金supported by the Mega-Projection of National Key Technology R & D Program for the 11th Five-Year Plan under Grant No.2006BAD10A14
文摘Liriomyza huidobrensis （Blanchard） is an important vegetable pest of pathology. In order to improve the accuracy of prediction of Liriomyza huidobrensis and to control the Liriomyza huidobrensis effectively, this paper presents a new prediction model by principal components analysis （PCA） and back propagation artificial neural network （BP-ANN） methods. The historical data from 1999 to 2007 on population occurrence are analyzed in order to find out a non-linear relationship between the pest occurrence and the meteorological factors. And then by using analysis results, the prediction model of Liriomyza huidobrensis occurrence in Jianshui in Yunnan is built. The new model has successfully applied to verify the paddy stem borer population occurrence in 2006. Test results show that the new prediction model with BP-ANN and PCA can improve the prediction accuracy.
基金supported by the National Natural Science Foundation of China under Grant No. 30525030, 60701015, and 60736029.
文摘Abstract-Common spatial pattern （CSP） algorithm is a successful tool in feature estimate of brain-computer interface （BCI）. However, CSP is sensitive to outlier and may result in poor outcomes since it is based on pooling the covariance matrices of trials. In this paper, we propose a simple yet effective approach, named common spatial pattern ensemble （CSPE） classifier, to improve CSP performance. Through division of recording channels, multiple CSP filters are constructed. By projection, log-operation, and subtraction on the original signal, an ensemble classifier, majority voting, is achieved and outlier contaminations are alleviated. Experiment results demonstrate that the proposed CSPE classifier is robust to various artifacts and can achieve an average accuracy of 83.02%.
