In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of ind...In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of independent variables （the microwave power, the acting time and the rotational frequency） for microwave drying of selenium-rich slag. The optimum operating conditions obtained from the quadratic form of the RSM are： the microwave power of 14.97 kW, the acting time of 89.58 min, the rotational frequency of 10.94 Hz, and the temperature of 136.407 ℃. The relative dehydration rate of 97.1895% is obtained. Under the optimum operating conditions, the incremental improved BP neural network prediction model can predict the drying process results and different effects on the results of the independent variables. The verification experiments demonstrate the prediction accuracy of the network, and the mean squared error is 0.16. The optimized results indicate that RSM can optimize the experimental conditions within much more broad range by considering the combination of factors and the neural network model can predict the results effectively and provide the theoretical guidance for the follow-up production process.展开更多
Molten iron temperature as well as Si, P, and S contents is the most essential molten iron quality （MIQ） indices in the blast furnace （BF） ironmaking, which requires strict monitoring during the whole ironmaking p...Molten iron temperature as well as Si, P, and S contents is the most essential molten iron quality （MIQ） indices in the blast furnace （BF） ironmaking, which requires strict monitoring during the whole ironmaking production. However, these MIQ parameters are difficult to be directly measured online, and large-time delay exists in off-line analysis through laboratory sampling. Focusing on the practical challenge, a data-driven modeling method was presented for the prediction of MIQ using the improved muhivariable incremental random vector functional-link net- works （M-I-RVFLNs）. Compared with the conventional random vector functional-link networks （RVFLNs） and the online sequential RVFLNs, the M-I-RVFLNs have solved the problem of deciding the optimal number of hidden nodes and overcome the overfitting problems. Moreover, the proposed M I RVFLNs model has exhibited the potential for multivariable prediction of the MIQ and improved the terminal condition for the multiple-input multiple-out- put （MIMO） dynamic system, which is suitable for the BF ironmaking process in practice. Ultimately, industrial experiments and contrastive researches have been conducted on the BF No. 2 in Liuzhou Iron and Steel Group Co. Ltd. of China using the proposed method, and the results demonstrate that the established model produces better estima ting accuracy than other MIQ modeling methods.展开更多
At present,there are few studies on the comprehensive evaluation of green power grid development in China,and all aspects of green power grid need to be evaluated.Therefore,this paper studies the green development lev...At present,there are few studies on the comprehensive evaluation of green power grid development in China,and all aspects of green power grid need to be evaluated.Therefore,this paper studies the green development level of power distribution network.This paper proposes a multi-level fuzzy comprehensive evaluation method,which first needs to classify the influencing factors.Therefore,this paper constructs an indicator system for the evaluation of green development of power distribution network from three dimensions.In order to avoid the influence of subjective factors,this paper adopts the model combining analytic hierarchy process and entropy weight method to give weight to indexes.Finally,five typical regions are selected for empirical analysis.The results show that the model established in this paper can reflect the green development level of power distribution network in each region and put forward relevant improvement suggestions for each region.展开更多
This paper studies approximation capability to L^2（Rd） functions of incremental constructive feedforward neural networks （FNN） with random hidden units. Two kinds of therelayered feedforward neural networks are co...This paper studies approximation capability to L^2（Rd） functions of incremental constructive feedforward neural networks （FNN） with random hidden units. Two kinds of therelayered feedforward neural networks are considered： radial basis function （RBF） neural networks and translation and dilation invariant （TDI） neural networks. In comparison with conventional methods that existence approach is mainly used in approximation theories for neural networks, we follow a constructive approach to prove that one may simply randomly choose parameters of hidden units and then adjust the weights between the hidden units and the output unit to make the neural network approximate any function in L2 （Rd） to any accuracy. Our result shows given any non-zero activation function g ： R＋ → R and g（｜｜x｜｜R^d） ∈ L^2（Rd） for RBF hidden units, or any non-zero activation function g（x） ∈ L^2（R^d） for TDI hidden units, the incremental network function fn with randomly generated hidden units converges to any target function in L2 （R^d） with probability one as the number of hidden units n → ∞, if one only properly adjusts the weights between the hidden units and output unit.展开更多
基金Project(50734007) supported by the National Natural Science Foundation of China
文摘In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of independent variables （the microwave power, the acting time and the rotational frequency） for microwave drying of selenium-rich slag. The optimum operating conditions obtained from the quadratic form of the RSM are： the microwave power of 14.97 kW, the acting time of 89.58 min, the rotational frequency of 10.94 Hz, and the temperature of 136.407 ℃. The relative dehydration rate of 97.1895% is obtained. Under the optimum operating conditions, the incremental improved BP neural network prediction model can predict the drying process results and different effects on the results of the independent variables. The verification experiments demonstrate the prediction accuracy of the network, and the mean squared error is 0.16. The optimized results indicate that RSM can optimize the experimental conditions within much more broad range by considering the combination of factors and the neural network model can predict the results effectively and provide the theoretical guidance for the follow-up production process.
基金Item Sponsored by National Natural Science Foundation of China(61290323,61333007,61473064)Fundamental Research Funds for Central Universities of China(N130108001)+1 种基金National High Technology Research and Development Program of China(2015AA043802)General Project on Scientific Research for Education Department of Liaoning Province of China(L20150186)
文摘Molten iron temperature as well as Si, P, and S contents is the most essential molten iron quality （MIQ） indices in the blast furnace （BF） ironmaking, which requires strict monitoring during the whole ironmaking production. However, these MIQ parameters are difficult to be directly measured online, and large-time delay exists in off-line analysis through laboratory sampling. Focusing on the practical challenge, a data-driven modeling method was presented for the prediction of MIQ using the improved muhivariable incremental random vector functional-link net- works （M-I-RVFLNs）. Compared with the conventional random vector functional-link networks （RVFLNs） and the online sequential RVFLNs, the M-I-RVFLNs have solved the problem of deciding the optimal number of hidden nodes and overcome the overfitting problems. Moreover, the proposed M I RVFLNs model has exhibited the potential for multivariable prediction of the MIQ and improved the terminal condition for the multiple-input multiple-out- put （MIMO） dynamic system, which is suitable for the BF ironmaking process in practice. Ultimately, industrial experiments and contrastive researches have been conducted on the BF No. 2 in Liuzhou Iron and Steel Group Co. Ltd. of China using the proposed method, and the results demonstrate that the established model produces better estima ting accuracy than other MIQ modeling methods.
基金Project Supported by the 2018 Key Projects of Philosophy and Social Sciences Research,Ministry of Education,China(Grant No.18JZD032).Topic:Research on Policies and Mechanisms of Building a Clean,Low-Carbon,Safe and Efficient Energy System.
文摘At present,there are few studies on the comprehensive evaluation of green power grid development in China,and all aspects of green power grid need to be evaluated.Therefore,this paper studies the green development level of power distribution network.This paper proposes a multi-level fuzzy comprehensive evaluation method,which first needs to classify the influencing factors.Therefore,this paper constructs an indicator system for the evaluation of green development of power distribution network from three dimensions.In order to avoid the influence of subjective factors,this paper adopts the model combining analytic hierarchy process and entropy weight method to give weight to indexes.Finally,five typical regions are selected for empirical analysis.The results show that the model established in this paper can reflect the green development level of power distribution network in each region and put forward relevant improvement suggestions for each region.
基金Supported by the National Nature Science Foundation of China (Grant No10871220)"Mathematics+X" of DLUT (Grant No842328)
文摘This paper studies approximation capability to L^2（Rd） functions of incremental constructive feedforward neural networks （FNN） with random hidden units. Two kinds of therelayered feedforward neural networks are considered： radial basis function （RBF） neural networks and translation and dilation invariant （TDI） neural networks. In comparison with conventional methods that existence approach is mainly used in approximation theories for neural networks, we follow a constructive approach to prove that one may simply randomly choose parameters of hidden units and then adjust the weights between the hidden units and the output unit to make the neural network approximate any function in L2 （Rd） to any accuracy. Our result shows given any non-zero activation function g ： R＋ → R and g（｜｜x｜｜R^d） ∈ L^2（Rd） for RBF hidden units, or any non-zero activation function g（x） ∈ L^2（R^d） for TDI hidden units, the incremental network function fn with randomly generated hidden units converges to any target function in L2 （R^d） with probability one as the number of hidden units n → ∞, if one only properly adjusts the weights between the hidden units and output unit.
