Using the hydrological and meteorological data in the Kaidu River Basin during 1957-2008, we simulated the hydro-climatic process by back-propagation artificial neural network （BPANN） based on wavelet analysis （WA...Using the hydrological and meteorological data in the Kaidu River Basin during 1957-2008, we simulated the hydro-climatic process by back-propagation artificial neural network （BPANN） based on wavelet analysis （WA）, and then compared the simulated results with those from a multiple linear regression （MLR）. The results show that the variation of runoff responded to regional climate change. The annual runoff （AR） was mainly affected by annual average temperature （AAT） and annual precipitation （AP）, which revealed different varia- tion patterns at five time scales. At the time scale of 32-years, AR presented a monotonically increasing trend with the similar trend of AAT and AP. But at the 2-year, 4- year, 8-year, and 16-year time-scale, AR presented non-linear variation with fluctuations of AAT and AP. Both MLR and BPANN successfully simulated the hydro- climatic process based on WA at each time scale, but the simulated effect from BPANN is better than that from MLR.展开更多
针对一类多输入多输出系统进行辨识,以"A simulation of the western basin of Lake Erie"为例,通过分析河流湖泊的水质特征,针对伊利湖湖泊水质建立数学模型,由于该环境系统为多输入多输出系统,文章采用了一种改进的BP神经...针对一类多输入多输出系统进行辨识,以"A simulation of the western basin of Lake Erie"为例,通过分析河流湖泊的水质特征,针对伊利湖湖泊水质建立数学模型,由于该环境系统为多输入多输出系统,文章采用了一种改进的BP神经网络算法,利用Matlab神经网络工具箱进行数据分析,绘出实际输出与模型输出的曲线以分析相关情况,检验建立的模型对于系统的辨识水平,给出传统BP网络和改进BP网络对该系统辨识的结果进行分析对比.文章还对不同噪声层次下的数据进行分析比较,并研究白噪声对于人工神经网络模型的影响.展开更多
This study was aimed to explore the associations between the combined effects of several polymorphisms in the PPAR-γ and RXR-α gene and environmental factors with the risk of metabolic syndrome by back-error propaga...This study was aimed to explore the associations between the combined effects of several polymorphisms in the PPAR-γ and RXR-α gene and environmental factors with the risk of metabolic syndrome by back-error propaga- tion artificial neural network （BPANN）. We established the model based on data gathered from metabolic syndrome patients （n = 1012） and normal controls （n = 1069） by BPANN. Mean impact value （MIV） for each input variable was calculated and the sequence of factors was sorted according to their absolute MIVs. Generalized multifactor dimensionality reduction （GMDR） confirmed a joint effect of PPAR-9＂ and RXR-a based on the results from BPANN. By BPANN analysis, the sequences according to the importance of metabolic syndrome risk fac- tors were in the order of body mass index （BMI）, serum adiponectin, rs4240711, gender, rs4842194, family history of type 2 diabetes, rs2920502, physical activity, alcohol drinking, rs3856806, family history of hypertension, rs1045570, rs6537944, age, rs17817276, family history of hyperlipidemia, smoking, rs1801282 and rs3132291. However, no polymorphism was statistically significant in multiple logistic regression analysis. After controlling for environmental factors, A1, A2, B1 and B2 （rs4240711, rs4842194, rs2920502 and rs3856806） models were the best models （cross-validation consistency 10/10, P = 0.0107） with the GMDR method. In conclusion, the interaction of the PPAR-γ and RXR-α gene could play a role in susceptibility to metabolic syndrome. A more realistic model is obtained by using BPANN to screen out determinants of diseases of multiple etiologies like metabolic syndrome.展开更多
文摘Using the hydrological and meteorological data in the Kaidu River Basin during 1957-2008, we simulated the hydro-climatic process by back-propagation artificial neural network （BPANN） based on wavelet analysis （WA）, and then compared the simulated results with those from a multiple linear regression （MLR）. The results show that the variation of runoff responded to regional climate change. The annual runoff （AR） was mainly affected by annual average temperature （AAT） and annual precipitation （AP）, which revealed different varia- tion patterns at five time scales. At the time scale of 32-years, AR presented a monotonically increasing trend with the similar trend of AAT and AP. But at the 2-year, 4- year, 8-year, and 16-year time-scale, AR presented non-linear variation with fluctuations of AAT and AP. Both MLR and BPANN successfully simulated the hydro- climatic process based on WA at each time scale, but the simulated effect from BPANN is better than that from MLR.
文摘针对一类多输入多输出系统进行辨识,以"A simulation of the western basin of Lake Erie"为例,通过分析河流湖泊的水质特征,针对伊利湖湖泊水质建立数学模型,由于该环境系统为多输入多输出系统,文章采用了一种改进的BP神经网络算法,利用Matlab神经网络工具箱进行数据分析,绘出实际输出与模型输出的曲线以分析相关情况,检验建立的模型对于系统的辨识水平,给出传统BP网络和改进BP网络对该系统辨识的结果进行分析对比.文章还对不同噪声层次下的数据进行分析比较,并研究白噪声对于人工神经网络模型的影响.
基金supported by the National Natural Science Foundation of China Grant No.30771858Jiangsu Provincial Natural Science Foundation Grant No.BK2007229Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)
文摘This study was aimed to explore the associations between the combined effects of several polymorphisms in the PPAR-γ and RXR-α gene and environmental factors with the risk of metabolic syndrome by back-error propaga- tion artificial neural network （BPANN）. We established the model based on data gathered from metabolic syndrome patients （n = 1012） and normal controls （n = 1069） by BPANN. Mean impact value （MIV） for each input variable was calculated and the sequence of factors was sorted according to their absolute MIVs. Generalized multifactor dimensionality reduction （GMDR） confirmed a joint effect of PPAR-9＂ and RXR-a based on the results from BPANN. By BPANN analysis, the sequences according to the importance of metabolic syndrome risk fac- tors were in the order of body mass index （BMI）, serum adiponectin, rs4240711, gender, rs4842194, family history of type 2 diabetes, rs2920502, physical activity, alcohol drinking, rs3856806, family history of hypertension, rs1045570, rs6537944, age, rs17817276, family history of hyperlipidemia, smoking, rs1801282 and rs3132291. However, no polymorphism was statistically significant in multiple logistic regression analysis. After controlling for environmental factors, A1, A2, B1 and B2 （rs4240711, rs4842194, rs2920502 and rs3856806） models were the best models （cross-validation consistency 10/10, P = 0.0107） with the GMDR method. In conclusion, the interaction of the PPAR-γ and RXR-α gene could play a role in susceptibility to metabolic syndrome. A more realistic model is obtained by using BPANN to screen out determinants of diseases of multiple etiologies like metabolic syndrome.
