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
The general regression neural network(GRNN) model was proposed to model and predict the length of day(LOD) change, which has very complicated time-varying characteristics. Meanwhile, considering that the axial atmosph...The general regression neural network(GRNN) model was proposed to model and predict the length of day(LOD) change, which has very complicated time-varying characteristics. Meanwhile, considering that the axial atmospheric angular momentum(AAM) function is tightly correlated with the LOD changes, it was introduced into the GRNN prediction model to further improve the accuracy of prediction. Experiments with the observational data of LOD changes show that the prediction accuracy of the GRNN model is 6.1% higher than that of BP network, and after introducing AAM function, the improvement of prediction accuracy further increases to 14.7%. The results show that the GRNN with AAM function is an effective prediction method for LOD changes.展开更多
For the investigation of mechanical properties of the bimrocks with high rock block proportion,a series of laboratory experiments,including resonance frequency and uniaxial compressive tests,are conducted on the 64 fa...For the investigation of mechanical properties of the bimrocks with high rock block proportion,a series of laboratory experiments,including resonance frequency and uniaxial compressive tests,are conducted on the 64 fabricated bimrocks specimens.The results demonstrate that dynamic elastic modulus is strongly correlated with the uniaxial compressive strength,elastic modulus and block proportions of the bimrocks.In addition,the density of the bimrocks has a good correlation with the mechanical properties of cases with varying block proportions.Thus,three crucial indices(including matrix strength)are used as basic input parameters for the prediction of the mechanical properties of the bimrocks.Other than adopting the traditional simple regression and multi-regression analyses,a new prediction model based on the optimized general regression neural network(GRNN)algorithm is proposed.Note that,the performance of the multi-regression prediction model is better than that of the simple regression model,owing to the consideration of various influencing factors.However,the comparison between model predictions indicates that the optimized GRNN model performs better than the multi-regression model does.Model validation and verification based on fabricated data and experimental data from the literature are performed to verify the predictability and applicability of the proposed optimized GRNN model.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Projects(U1231105,10878026)supported by the National Natural Science Foundation of China
文摘The general regression neural network(GRNN) model was proposed to model and predict the length of day(LOD) change, which has very complicated time-varying characteristics. Meanwhile, considering that the axial atmospheric angular momentum(AAM) function is tightly correlated with the LOD changes, it was introduced into the GRNN prediction model to further improve the accuracy of prediction. Experiments with the observational data of LOD changes show that the prediction accuracy of the GRNN model is 6.1% higher than that of BP network, and after introducing AAM function, the improvement of prediction accuracy further increases to 14.7%. The results show that the GRNN with AAM function is an effective prediction method for LOD changes.
基金Projects(51978669,U1734208)supported by the National Natural Science Foundation of ChinaProject(2018JJ3657)supported by Natural Science Foundation of Hunan Province,China
文摘For the investigation of mechanical properties of the bimrocks with high rock block proportion,a series of laboratory experiments,including resonance frequency and uniaxial compressive tests,are conducted on the 64 fabricated bimrocks specimens.The results demonstrate that dynamic elastic modulus is strongly correlated with the uniaxial compressive strength,elastic modulus and block proportions of the bimrocks.In addition,the density of the bimrocks has a good correlation with the mechanical properties of cases with varying block proportions.Thus,three crucial indices(including matrix strength)are used as basic input parameters for the prediction of the mechanical properties of the bimrocks.Other than adopting the traditional simple regression and multi-regression analyses,a new prediction model based on the optimized general regression neural network(GRNN)algorithm is proposed.Note that,the performance of the multi-regression prediction model is better than that of the simple regression model,owing to the consideration of various influencing factors.However,the comparison between model predictions indicates that the optimized GRNN model performs better than the multi-regression model does.Model validation and verification based on fabricated data and experimental data from the literature are performed to verify the predictability and applicability of the proposed optimized GRNN model.
