Firstly,general regression neural network(GRNN) was used for variable selection of key influencing factors of residential load(RL) forecasting.Secondly,the key influencing factors chosen by GRNN were used as the input...Firstly,general regression neural network(GRNN) was used for variable selection of key influencing factors of residential load(RL) forecasting.Secondly,the key influencing factors chosen by GRNN were used as the input and output terminals of urban and rural RL for simulating and learning.In addition,the suitable parameters of final model were obtained through applying the evidence theory to combine the optimization results which were calculated with the PSO method and the Bayes theory.Then,the model of PSO-Bayes least squares support vector machine(PSO-Bayes-LS-SVM) was established.A case study was then provided for the learning and testing.The empirical analysis results show that the mean square errors of urban and rural RL forecast are 0.02% and 0.04%,respectively.At last,taking a specific province RL in China as an example,the forecast results of RL from 2011 to 2015 were obtained.展开更多
UV absorption spectrum and neural network theory was used for the analysis of blood sugar concentration. Experimental investigation shows that absorption spectrum has the following characteristics in the wave band of ...UV absorption spectrum and neural network theory was used for the analysis of blood sugar concentration. Experimental investigation shows that absorption spectrum has the following characteristics in the wave band of 200-300 nm： （1） The absorption spectrum is of complex shape, there is more absorption peak in UV-band, it shows that there is a complex absorption phenomenon in blood group macromolecules; （2） The curve shapes of absorption spectra are similar to different samples ,the reason is that the spectrum is synthesis of some group macromolecules absorption spectrum; （3） There is a degree of displacement of absorption peak to different samples; （4） There is no significant correlation between absorbance and blood sugar concentration at 278 nm, but random. Based on the wave band of 280 nm to 300 nm, a neural network model was built to determine the blood sugar concentration.展开更多
基金Project(07JA790092) supported by the Research Grants from Humanities and Social Science Program of Ministry of Education of ChinaProject(10MR44) supported by the Fundamental Research Funds for the Central Universities in China
文摘Firstly,general regression neural network(GRNN) was used for variable selection of key influencing factors of residential load(RL) forecasting.Secondly,the key influencing factors chosen by GRNN were used as the input and output terminals of urban and rural RL for simulating and learning.In addition,the suitable parameters of final model were obtained through applying the evidence theory to combine the optimization results which were calculated with the PSO method and the Bayes theory.Then,the model of PSO-Bayes least squares support vector machine(PSO-Bayes-LS-SVM) was established.A case study was then provided for the learning and testing.The empirical analysis results show that the mean square errors of urban and rural RL forecast are 0.02% and 0.04%,respectively.At last,taking a specific province RL in China as an example,the forecast results of RL from 2011 to 2015 were obtained.
基金This work was supported by the National Natural Science Foundation of China （No. 10172043）.
文摘UV absorption spectrum and neural network theory was used for the analysis of blood sugar concentration. Experimental investigation shows that absorption spectrum has the following characteristics in the wave band of 200-300 nm： （1） The absorption spectrum is of complex shape, there is more absorption peak in UV-band, it shows that there is a complex absorption phenomenon in blood group macromolecules; （2） The curve shapes of absorption spectra are similar to different samples ,the reason is that the spectrum is synthesis of some group macromolecules absorption spectrum; （3） There is a degree of displacement of absorption peak to different samples; （4） There is no significant correlation between absorbance and blood sugar concentration at 278 nm, but random. Based on the wave band of 280 nm to 300 nm, a neural network model was built to determine the blood sugar concentration.
