By combining the distributed Kalman filter （DKF） with the back propagation neural network （BPNN）,a novel method is proposed to identify the bias of electrostatic suspended gyroscope （ESG）. Firstly,the data sets ...By combining the distributed Kalman filter （DKF） with the back propagation neural network （BPNN）,a novel method is proposed to identify the bias of electrostatic suspended gyroscope （ESG）. Firstly,the data sets of multi-measurements of the same ESG in different noise environments are ＂mapped＂ into a sensor network,and DKF with embedded consensus filters is then used to preprocess the data sets. After transforming the preprocessed results into the trained input and the desired output of neural network,BPNN with the learning rate and the momentum term is further utilized to identify the ESG bias. As demonstrated in the experiment,the proposed approach is effective for the model identification of the ESG bias.展开更多
For the high altitude cruising flight phase of a hypersonic cruise missile （HCM）, a relative motion mod- el between the missile and the target is established by defining virtual target and combining the theory of th...For the high altitude cruising flight phase of a hypersonic cruise missile （HCM）, a relative motion mod- el between the missile and the target is established by defining virtual target and combining the theory of the dif- ferential geometry with missile motion equations. Based on the model, the motion between the missile and the tar- get is considered as a single target differential game problem, and a new open-loop differential game midcourse guidance law （DGMGL） is deduced by solving the corresponding Hamiltonian Function. Meanwhile, a new struc- ture of a closed-loop DGMGL is presented and the training data for back propagation neural network （BPNN） are designed. By combining the theory of BPNN with the open-loop DGMGL obtained above, the law intelligence is realized. Finally, simulation is carried out and the validity of the law is testified.展开更多
This paper mainly discusses the selection of the technical parameters of fully mechanized top coal caving mining using the neural network technique. The comparison between computing results and experiment data shows t...This paper mainly discusses the selection of the technical parameters of fully mechanized top coal caving mining using the neural network technique. The comparison between computing results and experiment data shows that the set up neural network model has high accuracy and decision making benefit.展开更多
This paper puts forward a risk analysis model for software projects using enranced neural networks.The data for analysis are acquired through questionnaires from real software projects. To solve the multicollinearity ...This paper puts forward a risk analysis model for software projects using enranced neural networks.The data for analysis are acquired through questionnaires from real software projects. To solve the multicollinearity in software risks, the method of principal components analysis is adopted in the model to enhance network stability.To solve uncertainty of the neural networks structure and the uncertainty of the initial weights, genetic algorithms is employed.The experimental result reveals that the precision of software risk analysis can be improved by using the erhanced neural networks model.展开更多
Detailed experimental investigations were carried out for microwave pre-treatment of high ash Indian coal at high power level(900 W) in microwave oven. The microwave exposure times were fixed at60 s and 120 s. A rheol...Detailed experimental investigations were carried out for microwave pre-treatment of high ash Indian coal at high power level(900 W) in microwave oven. The microwave exposure times were fixed at60 s and 120 s. A rheology characteristic for microwave pre-treatment of coal-water slurry(CWS) was performed in an online Bohlin viscometer. The non-Newtonian character of the slurry follows the rheological model of Ostwald de Waele. The values of n and k vary from 0.31 to 0.64 and 0.19 to 0.81 Pa·sn,respectively. This paper presents an artificial neural network(ANN) model to predict the effects of operational parameters on apparent viscosity of CWS. A 4-2-1 topology with Levenberg-Marquardt training algorithm(trainlm) was selected as the controlled ANN. Mean squared error(MSE) of 0.002 and coefficient of multiple determinations(R^2) of 0.99 were obtained for the outperforming model. The promising values of correlation coefficient further confirm the robustness and satisfactory performance of the proposed ANN model.展开更多
In conventional chromite beneficiation plant, huge quantity of chromite is used to loss in the form of tailing. For recovery these valuable mineral, a gravity concentrator viz. wet shaking table was used.Optimisation ...In conventional chromite beneficiation plant, huge quantity of chromite is used to loss in the form of tailing. For recovery these valuable mineral, a gravity concentrator viz. wet shaking table was used.Optimisation along with performance prediction of the unit operation is necessary for efficient recovery.So, in this present study, an artificial neural network(ANN) modeling approach was attempted for predicting the performance of wet shaking table in terms of grade(%) and recovery(%). A three layer feed forward neural network(3:3–11–2:2) was developed by varying the major operating parameters such as wash water flow rate(L/min), deck tilt angle(degree) and slurry feed rate(L/h). The predicted value obtained by the neural network model shows excellent agreement with the experimental values.展开更多
The factors that influence underwater vehicle sonar self-noise are analyzed, and genetic algorithms and a back propagation (BP) neural network are combined to predict underwater vehicle sonar self-noise. The experimen...The factors that influence underwater vehicle sonar self-noise are analyzed, and genetic algorithms and a back propagation (BP) neural network are combined to predict underwater vehicle sonar self-noise. The experimental results demonstrate that underwater vehicle sonar self-noise can be predicted accurately by a GA-BP neural network that is based on actual underwater vehicle sonar data.展开更多
The diameter distribution function(DDF)is a crucial tool for accurately predicting stand carbon storage(CS).The current key issue,however,is how to construct a high-precision DDF based on stand factors,site quality,an...The diameter distribution function(DDF)is a crucial tool for accurately predicting stand carbon storage(CS).The current key issue,however,is how to construct a high-precision DDF based on stand factors,site quality,and aridity index to predict stand CS in multi-species mixed forests with complex structures.This study used data from70 survey plots for mixed broadleaf Populus davidiana and Betula platyphylla forests in the Mulan Rangeland State Forest,Hebei Province,China,to construct the DDF based on maximum likelihood estimation and finite mixture model(FMM).Ordinary least squares(OLS),linear seemingly unrelated regression(LSUR),and back propagation neural network(BPNN)were used to investigate the influences of stand factors,site quality,and aridity index on the shape and scale parameters of DDF and predicted stand CS of mixed broadleaf forests.The results showed that FMM accurately described the stand-level diameter distribution of the mixed P.davidiana and B.platyphylla forests;whereas the Weibull function constructed by MLE was more accurate in describing species-level diameter distribution.The combined variable of quadratic mean diameter(Dq),stand basal area(BA),and site quality improved the accuracy of the shape parameter models of FMM;the combined variable of Dq,BA,and De Martonne aridity index improved the accuracy of the scale parameter models.Compared to OLS and LSUR,the BPNN had higher accuracy in the re-parameterization process of FMM.OLS,LSUR,and BPNN overestimated the CS of P.davidiana but underestimated the CS of B.platyphylla in the large diameter classes(DBH≥18 cm).BPNN accurately estimated stand-and species-level CS,but it was more suitable for estimating stand-level CS compared to species-level CS,thereby providing a scientific basis for the optimization of stand structure and assessment of carbon sequestration capacity in mixed broadleaf forests.展开更多
A new method of artificial intelligence based on a new improved back propagation neural network （BPNN） algorithm is partially applied in the problem of image restoration. In order to over- come the inherited issues ...A new method of artificial intelligence based on a new improved back propagation neural network （BPNN） algorithm is partially applied in the problem of image restoration. In order to over- come the inherited issues in conventional back propagation algorithm i.e. slow convergence rate, longer training time, hard to achieve global minima etc. , different methods have been used including the introduction of dynamic learning rate and dynamic momentum coefficient etc. With the passage of time different techniques has been used to improve the dynamicity of these coefficients. The meth- od applied in this paper improves the effect of learning coefficient η by using a new way to modify the value dynamically during learning process. The experimental results show that this helps in im- proving the efficiency overall both in visual effect and quality analysis.展开更多
A new method of back propagation learning with respect to the problem of image restora- tion which is named as greyscale based learning in back propagation neural networks （BPNN） is in- vestigated. It is observed th...A new method of back propagation learning with respect to the problem of image restora- tion which is named as greyscale based learning in back propagation neural networks （BPNN） is in- vestigated. It is observed that by using this method the value of mean square error （MSE） decreases significantly. In addition, this method also gives good visual results when it is applied in image resto- ration problem. This method is also useful to tackle the inherited drawback of falling into local mini- ma by reducing its effect on overall system by bifurcating the learning locally different for different grey scale values. The performance of this algorithm has been studied in detail with different combi- nations of weights. In short, this algorithm provides much better results especially when compared with the simple back propagation algorithm with any further enhancements and without going for hy- brid solutions.展开更多
文摘By combining the distributed Kalman filter （DKF） with the back propagation neural network （BPNN）,a novel method is proposed to identify the bias of electrostatic suspended gyroscope （ESG）. Firstly,the data sets of multi-measurements of the same ESG in different noise environments are ＂mapped＂ into a sensor network,and DKF with embedded consensus filters is then used to preprocess the data sets. After transforming the preprocessed results into the trained input and the desired output of neural network,BPNN with the learning rate and the momentum term is further utilized to identify the ESG bias. As demonstrated in the experiment,the proposed approach is effective for the model identification of the ESG bias.
文摘For the high altitude cruising flight phase of a hypersonic cruise missile （HCM）, a relative motion mod- el between the missile and the target is established by defining virtual target and combining the theory of the dif- ferential geometry with missile motion equations. Based on the model, the motion between the missile and the tar- get is considered as a single target differential game problem, and a new open-loop differential game midcourse guidance law （DGMGL） is deduced by solving the corresponding Hamiltonian Function. Meanwhile, a new struc- ture of a closed-loop DGMGL is presented and the training data for back propagation neural network （BPNN） are designed. By combining the theory of BPNN with the open-loop DGMGL obtained above, the law intelligence is realized. Finally, simulation is carried out and the validity of the law is testified.
基金National Natural Science Foundation of China( 5 97340 90 )
文摘This paper mainly discusses the selection of the technical parameters of fully mechanized top coal caving mining using the neural network technique. The comparison between computing results and experiment data shows that the set up neural network model has high accuracy and decision making benefit.
文摘This paper puts forward a risk analysis model for software projects using enranced neural networks.The data for analysis are acquired through questionnaires from real software projects. To solve the multicollinearity in software risks, the method of principal components analysis is adopted in the model to enhance network stability.To solve uncertainty of the neural networks structure and the uncertainty of the initial weights, genetic algorithms is employed.The experimental result reveals that the precision of software risk analysis can be improved by using the erhanced neural networks model.
基金the sponsor CSIR (Council of Scientific and Industrial Research), New Delhi for their financial grant to carry out the present research work
文摘Detailed experimental investigations were carried out for microwave pre-treatment of high ash Indian coal at high power level(900 W) in microwave oven. The microwave exposure times were fixed at60 s and 120 s. A rheology characteristic for microwave pre-treatment of coal-water slurry(CWS) was performed in an online Bohlin viscometer. The non-Newtonian character of the slurry follows the rheological model of Ostwald de Waele. The values of n and k vary from 0.31 to 0.64 and 0.19 to 0.81 Pa·sn,respectively. This paper presents an artificial neural network(ANN) model to predict the effects of operational parameters on apparent viscosity of CWS. A 4-2-1 topology with Levenberg-Marquardt training algorithm(trainlm) was selected as the controlled ANN. Mean squared error(MSE) of 0.002 and coefficient of multiple determinations(R^2) of 0.99 were obtained for the outperforming model. The promising values of correlation coefficient further confirm the robustness and satisfactory performance of the proposed ANN model.
文摘In conventional chromite beneficiation plant, huge quantity of chromite is used to loss in the form of tailing. For recovery these valuable mineral, a gravity concentrator viz. wet shaking table was used.Optimisation along with performance prediction of the unit operation is necessary for efficient recovery.So, in this present study, an artificial neural network(ANN) modeling approach was attempted for predicting the performance of wet shaking table in terms of grade(%) and recovery(%). A three layer feed forward neural network(3:3–11–2:2) was developed by varying the major operating parameters such as wash water flow rate(L/min), deck tilt angle(degree) and slurry feed rate(L/h). The predicted value obtained by the neural network model shows excellent agreement with the experimental values.
文摘The factors that influence underwater vehicle sonar self-noise are analyzed, and genetic algorithms and a back propagation (BP) neural network are combined to predict underwater vehicle sonar self-noise. The experimental results demonstrate that underwater vehicle sonar self-noise can be predicted accurately by a GA-BP neural network that is based on actual underwater vehicle sonar data.
基金funded by the National Key Research and Development Program of China(No.2022YFD2200503-02)。
文摘The diameter distribution function(DDF)is a crucial tool for accurately predicting stand carbon storage(CS).The current key issue,however,is how to construct a high-precision DDF based on stand factors,site quality,and aridity index to predict stand CS in multi-species mixed forests with complex structures.This study used data from70 survey plots for mixed broadleaf Populus davidiana and Betula platyphylla forests in the Mulan Rangeland State Forest,Hebei Province,China,to construct the DDF based on maximum likelihood estimation and finite mixture model(FMM).Ordinary least squares(OLS),linear seemingly unrelated regression(LSUR),and back propagation neural network(BPNN)were used to investigate the influences of stand factors,site quality,and aridity index on the shape and scale parameters of DDF and predicted stand CS of mixed broadleaf forests.The results showed that FMM accurately described the stand-level diameter distribution of the mixed P.davidiana and B.platyphylla forests;whereas the Weibull function constructed by MLE was more accurate in describing species-level diameter distribution.The combined variable of quadratic mean diameter(Dq),stand basal area(BA),and site quality improved the accuracy of the shape parameter models of FMM;the combined variable of Dq,BA,and De Martonne aridity index improved the accuracy of the scale parameter models.Compared to OLS and LSUR,the BPNN had higher accuracy in the re-parameterization process of FMM.OLS,LSUR,and BPNN overestimated the CS of P.davidiana but underestimated the CS of B.platyphylla in the large diameter classes(DBH≥18 cm).BPNN accurately estimated stand-and species-level CS,but it was more suitable for estimating stand-level CS compared to species-level CS,thereby providing a scientific basis for the optimization of stand structure and assessment of carbon sequestration capacity in mixed broadleaf forests.
基金Supported by the National Natural Science Foundation of China (60772066)Higher Education Commission of Pakistan
文摘A new method of artificial intelligence based on a new improved back propagation neural network （BPNN） algorithm is partially applied in the problem of image restoration. In order to over- come the inherited issues in conventional back propagation algorithm i.e. slow convergence rate, longer training time, hard to achieve global minima etc. , different methods have been used including the introduction of dynamic learning rate and dynamic momentum coefficient etc. With the passage of time different techniques has been used to improve the dynamicity of these coefficients. The meth- od applied in this paper improves the effect of learning coefficient η by using a new way to modify the value dynamically during learning process. The experimental results show that this helps in im- proving the efficiency overall both in visual effect and quality analysis.
基金Supported by the National Natural Science Foundation of China(60772066)Higher Education Commission,Pakistan
文摘A new method of back propagation learning with respect to the problem of image restora- tion which is named as greyscale based learning in back propagation neural networks （BPNN） is in- vestigated. It is observed that by using this method the value of mean square error （MSE） decreases significantly. In addition, this method also gives good visual results when it is applied in image resto- ration problem. This method is also useful to tackle the inherited drawback of falling into local mini- ma by reducing its effect on overall system by bifurcating the learning locally different for different grey scale values. The performance of this algorithm has been studied in detail with different combi- nations of weights. In short, this algorithm provides much better results especially when compared with the simple back propagation algorithm with any further enhancements and without going for hy- brid solutions.
