In order to extract deterministic component from trend nonstationary time series, regression analysis by Akaike Information Criterion (AIC) for segment size and mean size of cocoon filament is introduced, and determin...In order to extract deterministic component from trend nonstationary time series, regression analysis by Akaike Information Criterion (AIC) for segment size and mean size of cocoon filament is introduced, and deterministic component is extracted from size series of cocoon filament by analysis result. Experiments of simulating deterministic components on 9 cocoon categories are carried out, and experimental result is analyzed. Through analysis and experiment, it is known that selecting the order and coefficients of regression equation by AIC is beneficial to accurately describe the relation between segment value and mean value. This study is also useful for pretreatment of nonstationary time series.展开更多
Statistical learning and recognition methods were used to extract the characteristics of size series measurements of cocoon filaments that are non-stationary in terms of mean and auto-covariance, by using the time var...Statistical learning and recognition methods were used to extract the characteristics of size series measurements of cocoon filaments that are non-stationary in terms of mean and auto-covariance, by using the time varying parameter auto-regressive （TVPAR） model. After the system was taught to recognize the size data, the system correctly recognized the size of series of cocoon filaments as much as 96.95% of the time for a single series and 98.72% of the time for the mean of two series. The correct recognition rate was higher after suitable filtering. The theory and method can be used to analyze other types of non-stationary finite length time series.展开更多
基金the Ministry of Education,Science ,Sports and Culture ,Japan, Grant-in-Aid for Scientific Research (B) ,2005,(No.17300228)
文摘In order to extract deterministic component from trend nonstationary time series, regression analysis by Akaike Information Criterion (AIC) for segment size and mean size of cocoon filament is introduced, and deterministic component is extracted from size series of cocoon filament by analysis result. Experiments of simulating deterministic components on 9 cocoon categories are carried out, and experimental result is analyzed. Through analysis and experiment, it is known that selecting the order and coefficients of regression equation by AIC is beneficial to accurately describe the relation between segment value and mean value. This study is also useful for pretreatment of nonstationary time series.
基金Supported by the Natural Science Foundation of Jiangsu Province, China (No. L0313419913)
文摘Statistical learning and recognition methods were used to extract the characteristics of size series measurements of cocoon filaments that are non-stationary in terms of mean and auto-covariance, by using the time varying parameter auto-regressive （TVPAR） model. After the system was taught to recognize the size data, the system correctly recognized the size of series of cocoon filaments as much as 96.95% of the time for a single series and 98.72% of the time for the mean of two series. The correct recognition rate was higher after suitable filtering. The theory and method can be used to analyze other types of non-stationary finite length time series.
