A nonparametric structural damage detection methodology based on neuralnetworks method is presented for health monitoring of structure-unknown systems. In this approachappropriate neural networks are trained by use of...A nonparametric structural damage detection methodology based on neuralnetworks method is presented for health monitoring of structure-unknown systems. In this approachappropriate neural networks are trained by use of the modal test data from a 'healthy' structure.The trained networks which are subsequently fed with vibration measurements from the same structurein different stages have the capability of recognizing the location and the content of structuraldamage and thereby can monitor the health of the structure. A modified back-propagation neuralnetwork is proposed to solve the two practical problems encountered by the traditionalback-propagation method, i.e., slow learning progress and convergence to a false local minimum.Various training algorithms, types of the input layer and numbers of the nodes in the input layerare considered. Numerical example results from a 5-degree-of-freedom spring-mass structure andanalyses on the experimental data of an actual 5-storey-steel-frame demonstrate thatneural-networks-based method is a robust procedure and a practical tool for the detection ofstructural damage, and that the modified back-propagation algorithm could improve the computationalefficiency as well as the accuracy of detection.展开更多
Objective To correct the nonlinear error of sensor output,a new approach to sensor inverse modeling based on Back-Propagation Fuzzy Logical System(BP FS) is presented.Methods The BP FS is a computationally efficient n...Objective To correct the nonlinear error of sensor output,a new approach to sensor inverse modeling based on Back-Propagation Fuzzy Logical System(BP FS) is presented.Methods The BP FS is a computationally efficient nonlinear universal approximator,which is capable of implementing complex nonlinear mapping from its input pattern space to the output with fast convergence speed.Results The neuro-fuzzy hybrid system,i.e.BP FS,is then applied to construct nonlinear inverse model of pressure sensor.The experimental results show that the proposed inverse modeling method automatically compensates the associated nonlinear error in pressure estimation,and thus the performance of pressure sensor is significantly improved.Conclusion The proposed method can be widely used in nonlinearity correction of various kinds of sensors to compensate the effects of nonlinearity and temperature on sensor output.展开更多
Phase pure ZrB2-SiC composite powders were prepared after 1 450℃/3 h via carbothermal reduction route,by using ZrSiO4,B2O3 and carbon as the raw materials.The influences of firing temperature as well as the type and ...Phase pure ZrB2-SiC composite powders were prepared after 1 450℃/3 h via carbothermal reduction route,by using ZrSiO4,B2O3 and carbon as the raw materials.The influences of firing temperature as well as the type and amount of additive on the phase composition of final products were detailedly investigated.The results indicated that the onset formation temperature of ZrB2-SiC was reduced to 1 400℃by the present conditions,and oxide additive(including CoSO4·7H2O,Y2O3 and TiO2)was effective in enhancing the decomposition of raw ZrSiO4,therefore accelerating the synthesis of ZrB2-SiC.Moreover,microstructural observation showed that the as-prepared ZrB2 and SiC respectively had well-defined hexagonal columnar and fibrous morphology.Furthermore,the methodology of back-propagation artificial neural networks(BP-ANNs)was adopted to establish a model for predicting the reaction extent(e g,the content of ZrB2-SiC in final product)in terms of various processing conditions.The results predicted by the as-established BP-ANNs model matched well with that of testing experiment(with a mean square error in 10^(-3) degree),verifying good effectiveness of the proposed strategy.展开更多
This paper presents a back-propagation neural network model for sound quality prediction (BPNN-SQP) of multiple working conditions’ vehicle interior noise. According to the standards and regulations, four kinds of ve...This paper presents a back-propagation neural network model for sound quality prediction (BPNN-SQP) of multiple working conditions’ vehicle interior noise. According to the standards and regulations, four kinds of vehicle interior noises under operating conditions, including idle, constant speed, accelerating and braking, are acquired. The objective psychoacoustic parameters and subjective annoyance results are respectively used as the input and output of the BPNN-SQP model. With correlation analysis and significance test, some psychoacoustic parameters, such as loudness, A-weighted sound pressure level, roughness, articulation index and sharpness, are selected for modeling. The annoyance values of unknown noise samples estimated by the BPNN-SQP model are highly correlated with the subjective annoyances. Conclusion can be drawn that the proposed BPNN-SQP model has good generalization ability and can be applied in sound quality prediction of vehicle interior noise under multiple working conditions.展开更多
In this work, a gradient method with momentum for BP neural networks is considered. The momentum coefficient is chosen in an adaptive manner to accelerate and stabilize the learning procedure of the network weights. C...In this work, a gradient method with momentum for BP neural networks is considered. The momentum coefficient is chosen in an adaptive manner to accelerate and stabilize the learning procedure of the network weights. Corresponding convergence results are proved.展开更多
The back-propagation neural network(BPNN) is a well-known multi-layer feed-forward neural network which is trained by the error reverse propagation algorithm. It is very suitable for the complex of short-term traffic ...The back-propagation neural network(BPNN) is a well-known multi-layer feed-forward neural network which is trained by the error reverse propagation algorithm. It is very suitable for the complex of short-term traffic flow forecasting; however, BPNN is easy to fall into local optimum and slow convergence. In order to overcome these deficiencies, a new approach called social emotion optimization algorithm(SEOA) is proposed in this paper to optimize the linked weights and thresholds of BPNN. Each individual in SEOA represents a BPNN. The availability of the proposed forecasting models is proved with the actual traffic flow data of the 2 nd Ring Road of Beijing. Experiment of results show that the forecasting accuracy of SEOA is improved obviously as compared with the accuracy of particle swarm optimization back-propagation(PSOBP) and simulated annealing particle swarm optimization back-propagation(SAPSOBP) models. Furthermore, since SEOA does not respond to the negative feedback information, Metropolis rule is proposed to give consideration to both positive and negative feedback information and diversify the adjustment methods. The modified BPNN model, in comparison with social emotion optimization back-propagation(SEOBP) model, is more advantageous to search the global optimal solution. The accuracy of Metropolis rule social emotion optimization back-propagation(MRSEOBP) model is improved about 19.54% as compared with that of SEOBP model in predicting the dramatically changing data.展开更多
Accurate prediction of the heat load is the basic premise of intelligent regulation of the heating system,which helps to realize effective management of heating,ventilation,air conditioning system.For the problem that...Accurate prediction of the heat load is the basic premise of intelligent regulation of the heating system,which helps to realize effective management of heating,ventilation,air conditioning system.For the problem that the weight of each influencing factor is not taken into account in the current heat load prediction and is not highly targeted,this article deeply explores the influence of different factors on the room heat load,and we propose a method to calculate room heat load prediction based on the combination of analytic hierarchy process(AHP)and back-propagation(BP)neural network.Firstly,eight environmental factors affecting the heat load are selected as prediction inputs through parametric analysis,and then the weights of each input are determined by AHP and normalize the prediction data by combining expert opinions,and finally do one-to-one training the quantified score and the room heat load to predict the future heat load by BP neural network.The simulation tests show that the mean absolute relative error(MARE)of the proposed prediction method is 5.40%.This article also verifies the influence of different expert opinions on the stability of the model.The results show that the proposed method can guarantee higher prediction accuracy and stability.展开更多
A short-term wind power prediction method is proposed in this paper with experimental results obtained from a wind farm located in Northeast China.In order to improve the accuracy of the prediction method using a trad...A short-term wind power prediction method is proposed in this paper with experimental results obtained from a wind farm located in Northeast China.In order to improve the accuracy of the prediction method using a traditional back-propagation(BP)neural network algorithm,the improved grey wolf optimization(IGWO)algorithm has been adopted to optimize its parameters.The performance of the proposed method has been evaluated by experiments.First,the features of the wind farm are described to show the fundamental information of the experiments.A single turbine with rated power of 1500 kW and power generation coefficient of 2.74 in the wind farm was introduced to show the technical details of the turbines.Original wind power data of the whole farm were preprocessed by using the quartile method to remove the abnormal data points.Then,the retained wind power data were predicted and analysed by using the proposed IGWO-BP algorithm.Analysis of the results proves the practicability and efficiency of the prediction model.Results show that the average accuracy of prediction is~11%greater than the traditional BP method.In this way,the proposed wind power prediction method can be adopted to improve the accuracy of prediction and to ensure the effective utilization of wind energy.展开更多
Materials data deep-excavation is very important in materials genome exploration.In order to carry out materials data deep-excavation in hot die steels and obtain the relationships among alloying elements,heat treatme...Materials data deep-excavation is very important in materials genome exploration.In order to carry out materials data deep-excavation in hot die steels and obtain the relationships among alloying elements,heat treatment parameters and materials properties,a 11×12×12×4 back-propagation(BP)artificial neural network(ANN)was set up.Alloying element contents,quenching and tempering temperatures were selected as input;hardness,tensile and yield strength were set as output parameters.The ANN shows a high fitting precision.The effects of alloying elements and heat treatment parameters on the properties of hot die steel were studied using this model.The results indicate that high temperature hardness increases with increasing alloying element content of C,Si,Mo,W,Ni,V and Cr to a maximum value and decreases with further increase in alloying element content.The ANN also predicts that the high temperature hardness will decrease with increasing quenching temperature,and possess an optimal value with increasing tempering temperature.This model provides a new tool for novel hot die steel design.展开更多
Water supply pipelines are the lifelines of a city. When pipelines burst, the burst site is difficult to locate by traditional methods such as manual tools or only by watching. In this paper, the burst site was iden...Water supply pipelines are the lifelines of a city. When pipelines burst, the burst site is difficult to locate by traditional methods such as manual tools or only by watching. In this paper, the burst site was identified using back-propagation (BP) artificial neural networks (ANN). The study is based on an indoor urban water supply model experiment. The key to appling BP ANN is to optimize the ANN's topological structure and learning parameters. This paper presents the optimizing method for a 3-layer BP neural network's topological structure and its learning parameters-learning ratio and the momentum factor. The indoor water supply pipeline model experimental results show that BP ANNs can be used to locate the burst point in urban water supply systems. The topological structure and learning parameters were optimized using the experimental results.展开更多
In this paper we consider the problem of sequential processing and present a sequen-tial model based on the back-propagation algorithm. This model is intended to deal with intrinsically sequential problems, such as wo...In this paper we consider the problem of sequential processing and present a sequen-tial model based on the back-propagation algorithm. This model is intended to deal with intrinsically sequential problems, such as word recognition, speech recognition,natural language understanding. This model can be used to train a network to learn the sequence of input patterns, in a fixed order or a random order. Besides, this mod-el is open- and partial-associative, characterized as 'recognizing while accumulating',which, as we argue, is mental cognition process oriented.展开更多
RNA interference(RNAi)is a phenomenon of gene silence induced by a double-stranded RNA(dsRNA)homologous to a target gene.RNAi can be used to identify the function of genes or to knock down the targeted genes.In RNAi t...RNA interference(RNAi)is a phenomenon of gene silence induced by a double-stranded RNA(dsRNA)homologous to a target gene.RNAi can be used to identify the function of genes or to knock down the targeted genes.In RNAi technology,19 bp double-stranded short interfering RNAs(siRNA)with characteristic 39 overhangs are usually used.The effects of siRNAs are quite varied due to the different choices in the sites of target mRNA.Moreover,there are many factors influencing siRNA activity and these factors are usually nonlinear.To find the motif features and the effect on siRNA activity,we carried out a feature extraction on some published experimental data and used these features to train a backpropagation neural network(BP NN).Then,we used the trained BP NN to predict siRNA activity.展开更多
This study established back-propagation neural networks(BPNNs)for evaluating the freshness of bighead carp(Hypophthalmichthys nobilis)heads during chilled storage via fluorescence spectroscopy using an excitation-emis...This study established back-propagation neural networks(BPNNs)for evaluating the freshness of bighead carp(Hypophthalmichthys nobilis)heads during chilled storage via fluorescence spectroscopy using an excitation-emission matrix(EEM).The total volatile basic nitrogen(TVB-N)and total aerobic count(TAC)of fish increased obviously during storage at 0,4,8,12,and 16°C,while sensory scores decreased with increasing storage time.The EEM fluorescence intensity was measured,and its change was correlated with the freshness indicators of the samples.Three characteristic components of EEM data were extracted by parallel factor analysis,and two freshness indicators were used to construct the EEM-BPNNs model.The results demonstrated that the relative errors of the EEM-BPNNs model for TVB-N and TAC were less than 14%.This result indicated that the EEM-BPNNs model could determine the freshness of fish in cold chains in a rapid and nondestructive way.展开更多
The Mean First-Passage Time (MFPT) and Stochastic Resonance (SR) of a stochastic tumor-immune model withnoise perturbation are discussed in this paper. Firstly, considering environmental perturbation, Gaussian whiteno...The Mean First-Passage Time (MFPT) and Stochastic Resonance (SR) of a stochastic tumor-immune model withnoise perturbation are discussed in this paper. Firstly, considering environmental perturbation, Gaussian whitenoise and Gaussian colored noise are introduced into a tumor growth model under immune surveillance. Asfollows, the long-time evolution of the tumor characterized by the Stationary Probability Density (SPD) and MFPTis obtained in theory on the basis of the Approximated Fokker-Planck Equation (AFPE). Herein the recurrenceof the tumor from the extinction state to the tumor-present state is more concerned in this paper. A moreefficient algorithmof Back-Propagation Neural Network (BPNN) is utilized in order to testify the correction of thetheoretical SPDandMFPT.With the existence of aweak signal, the functional relationship between Signal-to-NoiseRatio (SNR), noise intensities and correlation time is also studied. Numerical results show that both multiplicativeGaussian colored noise and additive Gaussian white noise can promote the extinction of the tumors, and themultiplicative Gaussian colored noise can lead to the resonance-like peak on MFPT curves, while the increasingintensity of the additiveGaussian white noise results in theminimum of MFPT. In addition, the correlation timesare negatively correlated with MFPT. As for the SNR, we find the intensities of both the Gaussian white noise andthe Gaussian colored noise, as well as their correlation intensity can induce SR. Especially, SNR is monotonouslyincreased in the case ofGaussian white noisewith the change of the correlation time.At last, the optimal parametersin BPNN structure are analyzed for MFPT from three aspects: the penalty factors, the number of neural networklayers and the number of nodes in each layer.展开更多
Damage assessment of the wing under blast wave is essential to the vulnerability reduction design of aircraft. This paper introduces a critical relative distance prediction method of aircraft wing damage based on the ...Damage assessment of the wing under blast wave is essential to the vulnerability reduction design of aircraft. This paper introduces a critical relative distance prediction method of aircraft wing damage based on the back-propagation artificial neural network(BP-ANN), which is trained by finite element simulation results. Moreover, the finite element method(FEM) for wing blast damage simulation has been validated by ground explosion tests and further used for damage mode determination and damage characteristics analysis. The analysis results indicate that the wing is more likely to be damaged when the root is struck from vertical directions than others for a small charge. With the increase of TNT equivalent charge, the main damage mode of the wing gradually changes from the local skin tearing to overall structural deformation and the overpressure threshold of wing damage decreases rapidly. Compared to the FEM-based damage assessment, the BP-ANN-based method can predict the wing damage under a random blast wave with an average relative error of 4.78%. The proposed method and conclusions can be used as a reference for damage assessment under blast wave and low-vulnerability design of aircraft structures.展开更多
Taking an industrial park as an example,this study aims to analyze the characteristics of a distribution network that incorporates distributed energy resources(DERs).The study begins by summarizing the key features of...Taking an industrial park as an example,this study aims to analyze the characteristics of a distribution network that incorporates distributed energy resources(DERs).The study begins by summarizing the key features of a distribution network with DERs based on recent power usage data.To predict and analyze the load growth of the industrial park,an improved back-propagation algorithm is employed.Furthermore,the study classifies users within the industrial park according to their specific power consumption and supply requirements.This user segmentation allows for the introduction of three constraints:node voltage,wire current,and capacity of DERs.By incorporating these constraints,the study constructs an optimization model for the distribution network in the industrial park,with the objective of minimizing the total operation and maintenance cost.The primary goal of these optimizations is to address the needs of DERs connected to the distribution network,while simultaneously mitigating their potential adverse impact on the network.Additionally,the study aims to enhance the overall energy efficiency of the industrial park through more efficient utilization of resources.展开更多
基金the National Natural Science Foundation of China (No.59908003)the Natural Science Foundation of Hubei Province (No.99J035)
文摘A nonparametric structural damage detection methodology based on neuralnetworks method is presented for health monitoring of structure-unknown systems. In this approachappropriate neural networks are trained by use of the modal test data from a 'healthy' structure.The trained networks which are subsequently fed with vibration measurements from the same structurein different stages have the capability of recognizing the location and the content of structuraldamage and thereby can monitor the health of the structure. A modified back-propagation neuralnetwork is proposed to solve the two practical problems encountered by the traditionalback-propagation method, i.e., slow learning progress and convergence to a false local minimum.Various training algorithms, types of the input layer and numbers of the nodes in the input layerare considered. Numerical example results from a 5-degree-of-freedom spring-mass structure andanalyses on the experimental data of an actual 5-storey-steel-frame demonstrate thatneural-networks-based method is a robust procedure and a practical tool for the detection ofstructural damage, and that the modified back-propagation algorithm could improve the computationalefficiency as well as the accuracy of detection.
基金This work was supported by National Natural Science Foundation of China(No.60276037).
文摘Objective To correct the nonlinear error of sensor output,a new approach to sensor inverse modeling based on Back-Propagation Fuzzy Logical System(BP FS) is presented.Methods The BP FS is a computationally efficient nonlinear universal approximator,which is capable of implementing complex nonlinear mapping from its input pattern space to the output with fast convergence speed.Results The neuro-fuzzy hybrid system,i.e.BP FS,is then applied to construct nonlinear inverse model of pressure sensor.The experimental results show that the proposed inverse modeling method automatically compensates the associated nonlinear error in pressure estimation,and thus the performance of pressure sensor is significantly improved.Conclusion The proposed method can be widely used in nonlinearity correction of various kinds of sensors to compensate the effects of nonlinearity and temperature on sensor output.
基金Funded by National Natural Science Foundation of China(Nos.51502212,51672194 and 51472184)Hubei Province Natural Science Foundation of China(No.2018CFB760)+1 种基金Program for Innovative Teams of Outstanding Young and Middle-aged Researchers in the Higher Education Institutions of Hubei Province(No.T201602)Key Program of Natural Science Foundation of Hubei Province(No.2017CFA004)
文摘Phase pure ZrB2-SiC composite powders were prepared after 1 450℃/3 h via carbothermal reduction route,by using ZrSiO4,B2O3 and carbon as the raw materials.The influences of firing temperature as well as the type and amount of additive on the phase composition of final products were detailedly investigated.The results indicated that the onset formation temperature of ZrB2-SiC was reduced to 1 400℃by the present conditions,and oxide additive(including CoSO4·7H2O,Y2O3 and TiO2)was effective in enhancing the decomposition of raw ZrSiO4,therefore accelerating the synthesis of ZrB2-SiC.Moreover,microstructural observation showed that the as-prepared ZrB2 and SiC respectively had well-defined hexagonal columnar and fibrous morphology.Furthermore,the methodology of back-propagation artificial neural networks(BP-ANNs)was adopted to establish a model for predicting the reaction extent(e g,the content of ZrB2-SiC in final product)in terms of various processing conditions.The results predicted by the as-established BP-ANNs model matched well with that of testing experiment(with a mean square error in 10^(-3) degree),verifying good effectiveness of the proposed strategy.
文摘This paper presents a back-propagation neural network model for sound quality prediction (BPNN-SQP) of multiple working conditions’ vehicle interior noise. According to the standards and regulations, four kinds of vehicle interior noises under operating conditions, including idle, constant speed, accelerating and braking, are acquired. The objective psychoacoustic parameters and subjective annoyance results are respectively used as the input and output of the BPNN-SQP model. With correlation analysis and significance test, some psychoacoustic parameters, such as loudness, A-weighted sound pressure level, roughness, articulation index and sharpness, are selected for modeling. The annoyance values of unknown noise samples estimated by the BPNN-SQP model are highly correlated with the subjective annoyances. Conclusion can be drawn that the proposed BPNN-SQP model has good generalization ability and can be applied in sound quality prediction of vehicle interior noise under multiple working conditions.
基金National Natural Science Foundation of China (10471017)Zhejiang Provincial Natural Science Foundation (Y606009)
文摘In this work, a gradient method with momentum for BP neural networks is considered. The momentum coefficient is chosen in an adaptive manner to accelerate and stabilize the learning procedure of the network weights. Corresponding convergence results are proved.
基金the Research of New Intelligent Integrated Transport Information System,Technical Plan Project of Binhai New District,Tianjin(No.2015XJR21017)
文摘The back-propagation neural network(BPNN) is a well-known multi-layer feed-forward neural network which is trained by the error reverse propagation algorithm. It is very suitable for the complex of short-term traffic flow forecasting; however, BPNN is easy to fall into local optimum and slow convergence. In order to overcome these deficiencies, a new approach called social emotion optimization algorithm(SEOA) is proposed in this paper to optimize the linked weights and thresholds of BPNN. Each individual in SEOA represents a BPNN. The availability of the proposed forecasting models is proved with the actual traffic flow data of the 2 nd Ring Road of Beijing. Experiment of results show that the forecasting accuracy of SEOA is improved obviously as compared with the accuracy of particle swarm optimization back-propagation(PSOBP) and simulated annealing particle swarm optimization back-propagation(SAPSOBP) models. Furthermore, since SEOA does not respond to the negative feedback information, Metropolis rule is proposed to give consideration to both positive and negative feedback information and diversify the adjustment methods. The modified BPNN model, in comparison with social emotion optimization back-propagation(SEOBP) model, is more advantageous to search the global optimal solution. The accuracy of Metropolis rule social emotion optimization back-propagation(MRSEOBP) model is improved about 19.54% as compared with that of SEOBP model in predicting the dramatically changing data.
基金supported by the Natural Science Foundation of China(No.61765012)the Natural Science Foundation of Inner Mongolia Autonomous Region(No.2021LHBS05005)+4 种基金the Science and Technology Research Project of Inner Mongolia Autonomous Region Higher Education(No.2021SHZR0620)the Inner Mongolia Autonomous Region 2017 Science and Technology Innovation Guidance Award Funding Projects(No.2017CXYD-2)the Natural Science Foundation of Inner Mongolia Autonomous Region(No.2019MS05008).The funders had no role in the design of the studyin the collection,analyses,or interpretation of datain the writing of the manuscript,or in the decision to publish the results.
文摘Accurate prediction of the heat load is the basic premise of intelligent regulation of the heating system,which helps to realize effective management of heating,ventilation,air conditioning system.For the problem that the weight of each influencing factor is not taken into account in the current heat load prediction and is not highly targeted,this article deeply explores the influence of different factors on the room heat load,and we propose a method to calculate room heat load prediction based on the combination of analytic hierarchy process(AHP)and back-propagation(BP)neural network.Firstly,eight environmental factors affecting the heat load are selected as prediction inputs through parametric analysis,and then the weights of each input are determined by AHP and normalize the prediction data by combining expert opinions,and finally do one-to-one training the quantified score and the room heat load to predict the future heat load by BP neural network.The simulation tests show that the mean absolute relative error(MARE)of the proposed prediction method is 5.40%.This article also verifies the influence of different expert opinions on the stability of the model.The results show that the proposed method can guarantee higher prediction accuracy and stability.
基金This work is supported by the science and technology research project of Jilin Provincial Department of Education(No.JJKH20210260KJ)This work is supported by the Jilin Provincial Department of Education(No.JJKH20210260KJ).
文摘A short-term wind power prediction method is proposed in this paper with experimental results obtained from a wind farm located in Northeast China.In order to improve the accuracy of the prediction method using a traditional back-propagation(BP)neural network algorithm,the improved grey wolf optimization(IGWO)algorithm has been adopted to optimize its parameters.The performance of the proposed method has been evaluated by experiments.First,the features of the wind farm are described to show the fundamental information of the experiments.A single turbine with rated power of 1500 kW and power generation coefficient of 2.74 in the wind farm was introduced to show the technical details of the turbines.Original wind power data of the whole farm were preprocessed by using the quartile method to remove the abnormal data points.Then,the retained wind power data were predicted and analysed by using the proposed IGWO-BP algorithm.Analysis of the results proves the practicability and efficiency of the prediction model.Results show that the average accuracy of prediction is~11%greater than the traditional BP method.In this way,the proposed wind power prediction method can be adopted to improve the accuracy of prediction and to ensure the effective utilization of wind energy.
文摘Materials data deep-excavation is very important in materials genome exploration.In order to carry out materials data deep-excavation in hot die steels and obtain the relationships among alloying elements,heat treatment parameters and materials properties,a 11×12×12×4 back-propagation(BP)artificial neural network(ANN)was set up.Alloying element contents,quenching and tempering temperatures were selected as input;hardness,tensile and yield strength were set as output parameters.The ANN shows a high fitting precision.The effects of alloying elements and heat treatment parameters on the properties of hot die steel were studied using this model.The results indicate that high temperature hardness increases with increasing alloying element content of C,Si,Mo,W,Ni,V and Cr to a maximum value and decreases with further increase in alloying element content.The ANN also predicts that the high temperature hardness will decrease with increasing quenching temperature,and possess an optimal value with increasing tempering temperature.This model provides a new tool for novel hot die steel design.
文摘Water supply pipelines are the lifelines of a city. When pipelines burst, the burst site is difficult to locate by traditional methods such as manual tools or only by watching. In this paper, the burst site was identified using back-propagation (BP) artificial neural networks (ANN). The study is based on an indoor urban water supply model experiment. The key to appling BP ANN is to optimize the ANN's topological structure and learning parameters. This paper presents the optimizing method for a 3-layer BP neural network's topological structure and its learning parameters-learning ratio and the momentum factor. The indoor water supply pipeline model experimental results show that BP ANNs can be used to locate the burst point in urban water supply systems. The topological structure and learning parameters were optimized using the experimental results.
文摘In this paper we consider the problem of sequential processing and present a sequen-tial model based on the back-propagation algorithm. This model is intended to deal with intrinsically sequential problems, such as word recognition, speech recognition,natural language understanding. This model can be used to train a network to learn the sequence of input patterns, in a fixed order or a random order. Besides, this mod-el is open- and partial-associative, characterized as 'recognizing while accumulating',which, as we argue, is mental cognition process oriented.
基金supported by the National Natural Science Foundation of China (Grant No.60471003).
文摘RNA interference(RNAi)is a phenomenon of gene silence induced by a double-stranded RNA(dsRNA)homologous to a target gene.RNAi can be used to identify the function of genes or to knock down the targeted genes.In RNAi technology,19 bp double-stranded short interfering RNAs(siRNA)with characteristic 39 overhangs are usually used.The effects of siRNAs are quite varied due to the different choices in the sites of target mRNA.Moreover,there are many factors influencing siRNA activity and these factors are usually nonlinear.To find the motif features and the effect on siRNA activity,we carried out a feature extraction on some published experimental data and used these features to train a backpropagation neural network(BP NN).Then,we used the trained BP NN to predict siRNA activity.
基金This study was supported by the Young Beijing Scholars Program and Beijing Agricultural Forestry Academy Foundation(QNJJ202218).
文摘This study established back-propagation neural networks(BPNNs)for evaluating the freshness of bighead carp(Hypophthalmichthys nobilis)heads during chilled storage via fluorescence spectroscopy using an excitation-emission matrix(EEM).The total volatile basic nitrogen(TVB-N)and total aerobic count(TAC)of fish increased obviously during storage at 0,4,8,12,and 16°C,while sensory scores decreased with increasing storage time.The EEM fluorescence intensity was measured,and its change was correlated with the freshness indicators of the samples.Three characteristic components of EEM data were extracted by parallel factor analysis,and two freshness indicators were used to construct the EEM-BPNNs model.The results demonstrated that the relative errors of the EEM-BPNNs model for TVB-N and TAC were less than 14%.This result indicated that the EEM-BPNNs model could determine the freshness of fish in cold chains in a rapid and nondestructive way.
基金National Natural Science Foundation of China(Nos.12272283,12172266).
文摘The Mean First-Passage Time (MFPT) and Stochastic Resonance (SR) of a stochastic tumor-immune model withnoise perturbation are discussed in this paper. Firstly, considering environmental perturbation, Gaussian whitenoise and Gaussian colored noise are introduced into a tumor growth model under immune surveillance. Asfollows, the long-time evolution of the tumor characterized by the Stationary Probability Density (SPD) and MFPTis obtained in theory on the basis of the Approximated Fokker-Planck Equation (AFPE). Herein the recurrenceof the tumor from the extinction state to the tumor-present state is more concerned in this paper. A moreefficient algorithmof Back-Propagation Neural Network (BPNN) is utilized in order to testify the correction of thetheoretical SPDandMFPT.With the existence of aweak signal, the functional relationship between Signal-to-NoiseRatio (SNR), noise intensities and correlation time is also studied. Numerical results show that both multiplicativeGaussian colored noise and additive Gaussian white noise can promote the extinction of the tumors, and themultiplicative Gaussian colored noise can lead to the resonance-like peak on MFPT curves, while the increasingintensity of the additiveGaussian white noise results in theminimum of MFPT. In addition, the correlation timesare negatively correlated with MFPT. As for the SNR, we find the intensities of both the Gaussian white noise andthe Gaussian colored noise, as well as their correlation intensity can induce SR. Especially, SNR is monotonouslyincreased in the case ofGaussian white noisewith the change of the correlation time.At last, the optimal parametersin BPNN structure are analyzed for MFPT from three aspects: the penalty factors, the number of neural networklayers and the number of nodes in each layer.
基金supported by the Natural Science Foundation of Shaanxi Province (Grant No. 2020JQ-122)the Fund support of Science and Technology on Transient Impact Laboratory。
文摘Damage assessment of the wing under blast wave is essential to the vulnerability reduction design of aircraft. This paper introduces a critical relative distance prediction method of aircraft wing damage based on the back-propagation artificial neural network(BP-ANN), which is trained by finite element simulation results. Moreover, the finite element method(FEM) for wing blast damage simulation has been validated by ground explosion tests and further used for damage mode determination and damage characteristics analysis. The analysis results indicate that the wing is more likely to be damaged when the root is struck from vertical directions than others for a small charge. With the increase of TNT equivalent charge, the main damage mode of the wing gradually changes from the local skin tearing to overall structural deformation and the overpressure threshold of wing damage decreases rapidly. Compared to the FEM-based damage assessment, the BP-ANN-based method can predict the wing damage under a random blast wave with an average relative error of 4.78%. The proposed method and conclusions can be used as a reference for damage assessment under blast wave and low-vulnerability design of aircraft structures.
基金supported by the Shanghai Municipal Social Science Foundation(No.2020BGL032).
文摘Taking an industrial park as an example,this study aims to analyze the characteristics of a distribution network that incorporates distributed energy resources(DERs).The study begins by summarizing the key features of a distribution network with DERs based on recent power usage data.To predict and analyze the load growth of the industrial park,an improved back-propagation algorithm is employed.Furthermore,the study classifies users within the industrial park according to their specific power consumption and supply requirements.This user segmentation allows for the introduction of three constraints:node voltage,wire current,and capacity of DERs.By incorporating these constraints,the study constructs an optimization model for the distribution network in the industrial park,with the objective of minimizing the total operation and maintenance cost.The primary goal of these optimizations is to address the needs of DERs connected to the distribution network,while simultaneously mitigating their potential adverse impact on the network.Additionally,the study aims to enhance the overall energy efficiency of the industrial park through more efficient utilization of resources.