This paper proposes an ensemble radial basis function neural network that selects important RBF subsets based on Pareto chart using Bootstrap samples.Then,the analysis of variance method is used to determine the choic...This paper proposes an ensemble radial basis function neural network that selects important RBF subsets based on Pareto chart using Bootstrap samples.Then,the analysis of variance method is used to determine the choice of the unequal/equal weights.The effectiveness of the proposed technique is illustrated with a micro-drilling process.The comparison results show that the proposed technique can not only improve the model prediction performance,but also generate a reliable scheme for quality design.展开更多
Based on the operation data from a certain wastewater treatment plant(WWTP) in northeast China, the models of back propagation neural network(BP NN) and radial basis function neural network(RBF NN) have been designed ...Based on the operation data from a certain wastewater treatment plant(WWTP) in northeast China, the models of back propagation neural network(BP NN) and radial basis function neural network(RBF NN) have been designed respectively and the ability of convergence and generalization has been analyzed separately. As for BP NN, the effects of numbers of layers and nodes have been studied; as for RBF NN, the influences of the number of nodes and the RBF′s width have been studied. It is concluded that BP NN has converged much slowly in comparison with RBF NN. The conclusion that the RBF NN is suitable for modeling activated sludge system has been drawn. An automatically optimum design program for RBF NN has been developed, through which the RBF NN model of traditional activated sludge system has been established.展开更多
Structural health monitoring is important to ensuring the health and safety of dams.An inverse analysis method based on a novel hybrid fireworks algorithm (FWA) and the radial basis function (RBF) model is proposed to...Structural health monitoring is important to ensuring the health and safety of dams.An inverse analysis method based on a novel hybrid fireworks algorithm (FWA) and the radial basis function (RBF) model is proposed to diagnose the health condition of concrete dams.The damage of concrete dams is diagnosed by identifying the elastic modulus of materials using the displacement changes at different reservoir water levels.FWA is a global optimization intelligent algorithm.The proposed hybrid algorithm combines the FWA with the pattern search algorithm, which has a high capability for local optimization.Examples of benchmark functions and pseudo-experiment examples of concrete dams illustrate that the hybrid FWA improves the convergence speed and robustness of the original algorithm.To address the time consumption problem, an RBF-based surrogate model was established to replace part of the finite element method in inverse analysis.Numerical examples of concrete dams illustrate that the use of an RBF-based surrogate model significantly reduces the computation time of inverse analysis with little influence on identification accuracy.The presented hybrid FWA combined with the RBF network can quickly and accurately determine the elastic modulus of materials, and then determine the health status of the concrete dam.展开更多
This paper presents a method of modeling a fuzzy system with fuzzy and nonlinear border,obtaining systematic structure by clustering analysis,applying BP network (BPN) to generate rule bases antecedent function and ...This paper presents a method of modeling a fuzzy system with fuzzy and nonlinear border,obtaining systematic structure by clustering analysis,applying BP network (BPN) to generate rule bases antecedent function and using RBF network (RBFN) to approximate each rules conclusion function not only because of efficient capability of approximation nonlinear function of BPN and RBFN but also because of quickness of training speed of RBFN. In addition,structure design and training of relevant networks are discussed in detail. Finally,the structure optimization and overstudy of RBFN are discussed.展开更多
基金This work was supported by the National Natural Science Foundation of China(NSFC)(grant numbers 71702072,71811540414,71401080,71871119,71771121)the Fundamental Research Funds for the Central Universities(grant number NR2019002)+1 种基金the Natural Science Foundation of Jiangsu Province-BK20170810The work of Professor Park was supported in part by the National Research Foundation of Korea grant funded by the Korea government(grant number NRF-2017R1A2B4004169).
文摘This paper proposes an ensemble radial basis function neural network that selects important RBF subsets based on Pareto chart using Bootstrap samples.Then,the analysis of variance method is used to determine the choice of the unequal/equal weights.The effectiveness of the proposed technique is illustrated with a micro-drilling process.The comparison results show that the proposed technique can not only improve the model prediction performance,but also generate a reliable scheme for quality design.
文摘Based on the operation data from a certain wastewater treatment plant(WWTP) in northeast China, the models of back propagation neural network(BP NN) and radial basis function neural network(RBF NN) have been designed respectively and the ability of convergence and generalization has been analyzed separately. As for BP NN, the effects of numbers of layers and nodes have been studied; as for RBF NN, the influences of the number of nodes and the RBF′s width have been studied. It is concluded that BP NN has converged much slowly in comparison with RBF NN. The conclusion that the RBF NN is suitable for modeling activated sludge system has been drawn. An automatically optimum design program for RBF NN has been developed, through which the RBF NN model of traditional activated sludge system has been established.
基金supported by the National Key Research and Development Program of China(Grants No.2016YFC0401600 and 2017YFC0404906)the National Natural Science Foundation of China(Grants No.51769033 and 51779035)the Fundamental Research Funds for the Central Universities(Grants No.DUT17ZD205 and DUT19LK14)
文摘Structural health monitoring is important to ensuring the health and safety of dams.An inverse analysis method based on a novel hybrid fireworks algorithm (FWA) and the radial basis function (RBF) model is proposed to diagnose the health condition of concrete dams.The damage of concrete dams is diagnosed by identifying the elastic modulus of materials using the displacement changes at different reservoir water levels.FWA is a global optimization intelligent algorithm.The proposed hybrid algorithm combines the FWA with the pattern search algorithm, which has a high capability for local optimization.Examples of benchmark functions and pseudo-experiment examples of concrete dams illustrate that the hybrid FWA improves the convergence speed and robustness of the original algorithm.To address the time consumption problem, an RBF-based surrogate model was established to replace part of the finite element method in inverse analysis.Numerical examples of concrete dams illustrate that the use of an RBF-based surrogate model significantly reduces the computation time of inverse analysis with little influence on identification accuracy.The presented hybrid FWA combined with the RBF network can quickly and accurately determine the elastic modulus of materials, and then determine the health status of the concrete dam.
基金This work was supported by the National Natural Science Foundation of China （51507015, 61773402, 61540037, 71271215, 61233008, 51425701, 70921001, 51577014）, the Natural Science Foundation of Hunan Province （2015JJ3008）, the Key Laboratory of Renewable Energy Electric-Technology of Hunan Province （2014ZNDL002）, and Hunan Province Science and Technology Program（2015NK3035）.
文摘This paper presents a method of modeling a fuzzy system with fuzzy and nonlinear border,obtaining systematic structure by clustering analysis,applying BP network (BPN) to generate rule bases antecedent function and using RBF network (RBFN) to approximate each rules conclusion function not only because of efficient capability of approximation nonlinear function of BPN and RBFN but also because of quickness of training speed of RBFN. In addition,structure design and training of relevant networks are discussed in detail. Finally,the structure optimization and overstudy of RBFN are discussed.
