A neural network model of key process parameters and forming quality is developed based on training samples which are obtained from the orthogonal experiment and the finite element numerical simulation. Optimization o...A neural network model of key process parameters and forming quality is developed based on training samples which are obtained from the orthogonal experiment and the finite element numerical simulation. Optimization of the process parameters is conducted using the genetic algorithm (GA). The experimental results have shown that a surface model of the neural network can describe the nonlinear implicit relationship between the parameters of the power spinning process:the wall margin and amount of expansion. It has been found that the process of determining spinning technological parameters can be accelerated using the optimization method developed based on the BP neural network and the genetic algorithm used for the process parameters of power spinning formation. It is undoubtedly beneficial towards engineering applications.展开更多
The alternate combinational approach of genetic algorithm and neural network （AGANN） has been presented to correct the systematic error of the density functional theory （DFT） calculation. It treats the DFT as a bl...The alternate combinational approach of genetic algorithm and neural network （AGANN） has been presented to correct the systematic error of the density functional theory （DFT） calculation. It treats the DFT as a black box and models the error through external statistical information. As a demonstration, the ACANN method has been applied in the correction of the lattice energies from the DFT calculation for 72 metal halides and hydrides. Through the AGANN correction, the mean absolute value of the relative errors of the calculated lattice energies to the experimental values decreases from 4.93% to 1.20% in the testing set. For comparison, the neural network approach reduces the mean value to 2.56%. And for the common combinational approach of genetic algorithm and neural network, the value drops to 2.15%. The multiple linear regression method almost has no correction effect here.展开更多
Today flotation column has become an acceptable means of froth flotation for a fairly broad range of applications, in particular the cleaning of sulfides. Even after having been used for several years in mineral proce...Today flotation column has become an acceptable means of froth flotation for a fairly broad range of applications, in particular the cleaning of sulfides. Even after having been used for several years in mineral processing plants, the full potential of the flotation column process is still not fully exploited. There is no prediction of process performance for the complete use of available control capabilities. The on-line estimation of grade usually requires a significant amount of work in maintenance and calibration of on-stream analyzers, in order to maintain good accuracy and high availability. These difficulties and the high cost of investment and maintenance of these devices have encouraged the approach of prediction of metal grade and recovery. In this paper, a new approach has been proposed for metallurgical performance prediction in flotation columns using Artificial Neural Network (ANN). Despite of the wide range of applications and flexibility of NNs, there is still no general framework or procedure through which the appropriate network for a specific task can be designed. Design and structural optimization of NNs is still strongly dependent upon the designer's experience. To mitigate this problem, a new method for the auto-design of NNs was used, based on Genetic Algorithm (GA). The new proposed method was evaluated by a case study in pilot plant flotation column at Sarcheshmeh copper plant. The chemical reagents dosage, froth height, air, wash water flow rates, gas holdup, Cu grade in the rougher feed, flotation column feed, column tail and final concentrate streams were used to the simulation by GANN. In this work, multi-layer NNs with Back Propagation (BP) algorithm with 8-17-10-2 and 8- 13-6-2 arrangements have been applied to predict the Cu and Mo grades and recoveries, respectively. The correlation coefficient (R) values for the testing sets for Cu and Mo grades were 0.93, 0.94 and for their recoveries were 0.93, 0.92, respectively. The results discussed in this paper indicate that the proposed model can be used to predict the Cu and Mo grades and recoveries with a reasonable error.展开更多
Identification simulation for dynamical system which is based on genetic algorithm (GA) and recurrent multilayer neural network (RMNN) is presented. In order to reduce the inputs of the model, RMNN which can remember ...Identification simulation for dynamical system which is based on genetic algorithm (GA) and recurrent multilayer neural network (RMNN) is presented. In order to reduce the inputs of the model, RMNN which can remember and store some previous parameters is used for identifier. And for its high efficiency and optimization, genetic algorithm is introduced into training RMNN. Simulation results show the effectiveness of the proposed scheme. Under the same training algorithm, the identification performance of RMNN is superior to that of nonrecurrent multilayer neural network (NRMNN).展开更多
According to the typical engineering samples, a neural net work model with genetic algorithm to optimize weight values is put forward to forecast the productivities and efficiencies of mining faces. By this model we c...According to the typical engineering samples, a neural net work model with genetic algorithm to optimize weight values is put forward to forecast the productivities and efficiencies of mining faces. By this model we can obtain the possible achievements of available equipment combinations under certain geological situations of fully-mechanized coal mining faces. Then theory of fuzzy selection is applied to evaluate the performance of each equipment combination. By detailed empirical analysis, this model integrates the functions of forecasting mining faces' achievements and selecting optimal equipment combination and is helpful to the decision of equipment combination for fully-mechanized coal mining.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
Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. Th...Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. The neural network nodes of the input layer can be calculated and simplified through rough sets theory; The neural network nodes of the middle layer are designed through genetic algorithms training; the neural network bottom-up weights and bias are obtained finally through the combination of genetic algorithms and BP algorithms. The analysis in this paper illustrates that the optimization method can improve the performance of the neural network fault diagnosis method greatly.展开更多
This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in boa...This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in board allocating of furniture production. In the experiment, the rectangular flake board of 3650 mm 1850 mm was used as raw material to allocate 100 sets of Table Bucked. The utilizing rate of the board reached 94.14 % and the calculating time was only 35 s. The experiment result proofed that the method by using the GA for optimizing the weights of the ANN can raise the utilizing rate of the board and can shorten the time of the design. At the same time, this method can simultaneously searched in many directions, thus greatly in-creasing the probability of finding a global optimum.展开更多
Accurate prediction of chemical composition of vacuum gas oil （VGO） is essential for the routine operation of refineries. In this work, a new approach for auto-design of artificial neural networks （ANN） based on a...Accurate prediction of chemical composition of vacuum gas oil （VGO） is essential for the routine operation of refineries. In this work, a new approach for auto-design of artificial neural networks （ANN） based on a genetic algorithm （GA） is developed for predicting VGO saturates. The number of neurons in the hidden layer, the momentum and the learning rates are determined by using the genetic algorithm. The inputs for the artificial neural networks model are five physical properties, namely, average boiling point, density, molecular weight, viscosity and refractive index. It is verified that the genetic algorithm could find the optimal structural parameters and training parameters of ANN. In addition, an artificial neural networks model based on a genetic algorithm was tested and the results indicated that the VGO saturates can be efficiently predicted. Compared with conventional artificial neural networks models, this approach can improve the prediction accuracy.展开更多
A new method based on the combination of a neural network and a genetic algorithm was proposed to rank the order of exploitation priority of coalbed methane reservoirs. The neural network was used to acquire the weigh...A new method based on the combination of a neural network and a genetic algorithm was proposed to rank the order of exploitation priority of coalbed methane reservoirs. The neural network was used to acquire the weights of reservoir parameters through sample training and genetic algorithm was used to optimize the initial connection weights of nerve cells in case the neural network fell into a local minimum. Additionally, subordinate functions of each parameter were established to normalize the actual values of parameters of coalbed methane reservoirs in the range between zero and unity. Eventually, evaluation values of all coalbed methane reservoirs could be obtained by using the comprehensive evaluation method, which is the basis to rank the coalbed methane reservoirs in the order of exploitation priority. The greater the evaluation value, the higher the exploitation priority. The ranking method was verified in this paper by ten exploited coalbed methane reservoirs in China. The evaluation results are in agreement with the actual exploitation cases. The method can ensure the truthfulness and credibility of the weights of parameters and avoid the subjectivity caused by experts. Furthermore, the probability of falling into local minima is reduced, because genetic the algorithm is used to optimize the neural network system.展开更多
The performance of deep learning(DL)networks has been increased by elaborating the network structures. However, the DL netowrks have many parameters, which have a lot of influence on the performance of the network. We...The performance of deep learning(DL)networks has been increased by elaborating the network structures. However, the DL netowrks have many parameters, which have a lot of influence on the performance of the network. We propose a genetic algorithm(GA) based deep belief neural network(DBNN) method for robot object recognition and grasping purpose. This method optimizes the parameters of the DBNN method, such as the number of hidden units, the number of epochs, and the learning rates, which would reduce the error rate and the network training time of object recognition. After recognizing objects, the robot performs the pick-andplace operations. We build a database of six objects for experimental purpose. Experimental results demonstrate that our method outperforms on the optimized robot object recognition and grasping tasks.展开更多
Aimed at the problems of premature and lower convergence of simple genetic algorithms （SGA）, three ideas --partition the whole search uniformly, multi-genetic operators and multi-populations evolving independently a...Aimed at the problems of premature and lower convergence of simple genetic algorithms （SGA）, three ideas --partition the whole search uniformly, multi-genetic operators and multi-populations evolving independently are introduced, and a grid-based pseudo-parallel genetic algorithms （GPPGA） is put forward. Thereafter, the analysis of premature and convergence of GPPGA is made. In the end, GPPGA is tested by both six-peak camel back function, Rosenbrock function and BP network. The result shows the feasibility and effectiveness of GPPGA in overcoming premature and improving convergence speed and accuracy.展开更多
We developed and tested an improved neural network to predict the average concentration of PM10(particulate matter with diameter smaller than 10 ?m) several hours in advance in summer in Beijing.A genetic algorithm op...We developed and tested an improved neural network to predict the average concentration of PM10(particulate matter with diameter smaller than 10 ?m) several hours in advance in summer in Beijing.A genetic algorithm optimization procedure for optimizing initial weights and thresholds of the neural network was also evaluated.This research was based upon the PM10 data from seven monitoring sites in Beijing urban region and meteorological observation data,which were recorded every 3 h during summer of 2002.Two neural network models were developed.Model I was built for predicting PM10 concentrations 3 h in advance while Model II for one day in advance.The predictions of both models were found to be consistent with observations.Percent errors in forecasting the numerical value were about 20.This brings us to the conclusion that short-term fluctuations of PM10 concentrations in Beijing urban region in summer are to a large extent driven by meteorological conditions.Moreover,the predicted results of Model II were compared with the ones provided by the Models-3 Community Multiscale Air Quality(CMAQ) modeling system.The mean relative errors of both models were 0.21 and 0.26,respectively.The performance of the neural network model was similar to numerical models,when applied to short-time prediction of PM10 concentration.展开更多
In energy dispersive X-ray fiuorescence(EDXRF), quantitative elemental content analysis becomes difficult due to the existence of the noise, the spectrum peak superposition, element matrix effect, etc. In this paper, ...In energy dispersive X-ray fiuorescence(EDXRF), quantitative elemental content analysis becomes difficult due to the existence of the noise, the spectrum peak superposition, element matrix effect, etc. In this paper, a hybrid approach of genetic algorithm(GA) and back propagation(BP) neural network is proposed without considering the complex relationship between the elemental content and peak intensity. The aim of GA-optimized BP is to get better network initial weights and thresholds. The starting point of this approach is that the reciprocal of the mean square error of the initialization BP neural network is set as the fitness value of the individuals in GA; and the initial weights and thresholds are replaced by individuals, then the optimal individual is searched by selecting, crossover and mutation operations, finally a new BP neural network model is established with the optimal initial weights and thresholds. The quantitative analysis results of titanium and iron contents in five types of mineral samples show that the relative errors of 76.7% samples are below 2%, compared to chemical analysis data, which demonstrates the effectiveness of the proposed method.展开更多
In this paper, a new neuroevolution algorithm (NEGA) for simultaneous evolution of both architectures and weights of neural networks is described. A whole new network encoding method is shown. The competing convention...In this paper, a new neuroevolution algorithm (NEGA) for simultaneous evolution of both architectures and weights of neural networks is described. A whole new network encoding method is shown. The competing conventions problem is solved absolutely. Heuristic methods are used to constrain the topology mutation probability and the trend of mutation kind choice. Also, the niching method is used to protect the network topologies evolution. The experiment results show the efficiency and rapidity of NEGA forcefully.展开更多
A real valued genetic algorithm(RVGA) for the optimization problem with continuous variables is proposed. It is composed of a simple and general purpose dynamic scaled fitness and selection operator, crossover opera...A real valued genetic algorithm(RVGA) for the optimization problem with continuous variables is proposed. It is composed of a simple and general purpose dynamic scaled fitness and selection operator, crossover operator, mutation operators and adaptive probabilities for these operators. The algorithm is tested by two generally used functions and is used in training a neural network for image recognition. Experimental results show that the algorithm is an efficient global optimization algorithm.展开更多
This paper investigated the resistance performance of a submersible surface ship(SSS)in different working cases and scales to analyze the hydrodynamic performance characteristics of an SSS at different speeds and divi...This paper investigated the resistance performance of a submersible surface ship(SSS)in different working cases and scales to analyze the hydrodynamic performance characteristics of an SSS at different speeds and diving depths for engineering applications.First,a hydrostatic resistance performance test of the SSS was carried out in a towing tank.Second,the scale effect of the hydrodynamic pressure coefficient and wave-making resistance was analyzed.The differences between the three-dimensional real-scale ship resistance prediction and numerical methods were explained.Finally,the advantages of genetic algorithm(GA)and neural network were combined to predict the resistance of SSS.Back propagation neural network(BPNN)and GA-BPNN were utilized to predict the SSS resistance.We also studied neural network parameter optimization,including connection weights and thresholds,using K-fold cross-validation.The results showed that when a SSS sails at low and medium speeds,the influence of various underwater cases on resistance is not obvious,while at high speeds,the resistance of water surface cases increases sharply with an increase in speed.After improving the weights and thresholds through K-fold cross-validation and GA,the prediction results of BPNN have high consistency with the actual values.The research results can provide a theoretical reference for the optimal design of the resistance of SSS in practical applications.展开更多
A direct feedback control system based on fuzzy recurrent neural network is proposed, and a method of training weights of fuzzy recurrent neural network was designed by applying modified contract mapping genetic algor...A direct feedback control system based on fuzzy recurrent neural network is proposed, and a method of training weights of fuzzy recurrent neural network was designed by applying modified contract mapping genetic algorithm. Computer simulation results indicate that fuzzy recurrent neural network controller has perfect dynamic and static performances .展开更多
基金Supported by the Natural Science Foundation of Shanxi Province Project(2012011023-2)
文摘A neural network model of key process parameters and forming quality is developed based on training samples which are obtained from the orthogonal experiment and the finite element numerical simulation. Optimization of the process parameters is conducted using the genetic algorithm (GA). The experimental results have shown that a surface model of the neural network can describe the nonlinear implicit relationship between the parameters of the power spinning process:the wall margin and amount of expansion. It has been found that the process of determining spinning technological parameters can be accelerated using the optimization method developed based on the BP neural network and the genetic algorithm used for the process parameters of power spinning formation. It is undoubtedly beneficial towards engineering applications.
基金supported by the National Basic Research Program of China (973 Program) (Grant No. G2009CB929300)the National Natural Science Foundation of China (Grant No. 60521001 and 60925016)
文摘The alternate combinational approach of genetic algorithm and neural network （AGANN） has been presented to correct the systematic error of the density functional theory （DFT） calculation. It treats the DFT as a black box and models the error through external statistical information. As a demonstration, the ACANN method has been applied in the correction of the lattice energies from the DFT calculation for 72 metal halides and hydrides. Through the AGANN correction, the mean absolute value of the relative errors of the calculated lattice energies to the experimental values decreases from 4.93% to 1.20% in the testing set. For comparison, the neural network approach reduces the mean value to 2.56%. And for the common combinational approach of genetic algorithm and neural network, the value drops to 2.15%. The multiple linear regression method almost has no correction effect here.
基金the support of the Department of Research and Development of Sarcheshmeh copper plants for this research
文摘Today flotation column has become an acceptable means of froth flotation for a fairly broad range of applications, in particular the cleaning of sulfides. Even after having been used for several years in mineral processing plants, the full potential of the flotation column process is still not fully exploited. There is no prediction of process performance for the complete use of available control capabilities. The on-line estimation of grade usually requires a significant amount of work in maintenance and calibration of on-stream analyzers, in order to maintain good accuracy and high availability. These difficulties and the high cost of investment and maintenance of these devices have encouraged the approach of prediction of metal grade and recovery. In this paper, a new approach has been proposed for metallurgical performance prediction in flotation columns using Artificial Neural Network (ANN). Despite of the wide range of applications and flexibility of NNs, there is still no general framework or procedure through which the appropriate network for a specific task can be designed. Design and structural optimization of NNs is still strongly dependent upon the designer's experience. To mitigate this problem, a new method for the auto-design of NNs was used, based on Genetic Algorithm (GA). The new proposed method was evaluated by a case study in pilot plant flotation column at Sarcheshmeh copper plant. The chemical reagents dosage, froth height, air, wash water flow rates, gas holdup, Cu grade in the rougher feed, flotation column feed, column tail and final concentrate streams were used to the simulation by GANN. In this work, multi-layer NNs with Back Propagation (BP) algorithm with 8-17-10-2 and 8- 13-6-2 arrangements have been applied to predict the Cu and Mo grades and recoveries, respectively. The correlation coefficient (R) values for the testing sets for Cu and Mo grades were 0.93, 0.94 and for their recoveries were 0.93, 0.92, respectively. The results discussed in this paper indicate that the proposed model can be used to predict the Cu and Mo grades and recoveries with a reasonable error.
文摘Identification simulation for dynamical system which is based on genetic algorithm (GA) and recurrent multilayer neural network (RMNN) is presented. In order to reduce the inputs of the model, RMNN which can remember and store some previous parameters is used for identifier. And for its high efficiency and optimization, genetic algorithm is introduced into training RMNN. Simulation results show the effectiveness of the proposed scheme. Under the same training algorithm, the identification performance of RMNN is superior to that of nonrecurrent multilayer neural network (NRMNN).
文摘According to the typical engineering samples, a neural net work model with genetic algorithm to optimize weight values is put forward to forecast the productivities and efficiencies of mining faces. By this model we can obtain the possible achievements of available equipment combinations under certain geological situations of fully-mechanized coal mining faces. Then theory of fuzzy selection is applied to evaluate the performance of each equipment combination. By detailed empirical analysis, this model integrates the functions of forecasting mining faces' achievements and selecting optimal equipment combination and is helpful to the decision of equipment combination for fully-mechanized coal mining.
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.
文摘Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. The neural network nodes of the input layer can be calculated and simplified through rough sets theory; The neural network nodes of the middle layer are designed through genetic algorithms training; the neural network bottom-up weights and bias are obtained finally through the combination of genetic algorithms and BP algorithms. The analysis in this paper illustrates that the optimization method can improve the performance of the neural network fault diagnosis method greatly.
基金This paper is supported by the Nature Science Foundation of Heilongjiang Province.
文摘This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in board allocating of furniture production. In the experiment, the rectangular flake board of 3650 mm 1850 mm was used as raw material to allocate 100 sets of Table Bucked. The utilizing rate of the board reached 94.14 % and the calculating time was only 35 s. The experiment result proofed that the method by using the GA for optimizing the weights of the ANN can raise the utilizing rate of the board and can shorten the time of the design. At the same time, this method can simultaneously searched in many directions, thus greatly in-creasing the probability of finding a global optimum.
文摘Accurate prediction of chemical composition of vacuum gas oil （VGO） is essential for the routine operation of refineries. In this work, a new approach for auto-design of artificial neural networks （ANN） based on a genetic algorithm （GA） is developed for predicting VGO saturates. The number of neurons in the hidden layer, the momentum and the learning rates are determined by using the genetic algorithm. The inputs for the artificial neural networks model are five physical properties, namely, average boiling point, density, molecular weight, viscosity and refractive index. It is verified that the genetic algorithm could find the optimal structural parameters and training parameters of ANN. In addition, an artificial neural networks model based on a genetic algorithm was tested and the results indicated that the VGO saturates can be efficiently predicted. Compared with conventional artificial neural networks models, this approach can improve the prediction accuracy.
基金EU-China Energy and Environment Programme(Europe Aid/120723/D/SV/CN)Research Fund for the Doctoral Program of Higher Education of China(20030425001)
文摘A new method based on the combination of a neural network and a genetic algorithm was proposed to rank the order of exploitation priority of coalbed methane reservoirs. The neural network was used to acquire the weights of reservoir parameters through sample training and genetic algorithm was used to optimize the initial connection weights of nerve cells in case the neural network fell into a local minimum. Additionally, subordinate functions of each parameter were established to normalize the actual values of parameters of coalbed methane reservoirs in the range between zero and unity. Eventually, evaluation values of all coalbed methane reservoirs could be obtained by using the comprehensive evaluation method, which is the basis to rank the coalbed methane reservoirs in the order of exploitation priority. The greater the evaluation value, the higher the exploitation priority. The ranking method was verified in this paper by ten exploited coalbed methane reservoirs in China. The evaluation results are in agreement with the actual exploitation cases. The method can ensure the truthfulness and credibility of the weights of parameters and avoid the subjectivity caused by experts. Furthermore, the probability of falling into local minima is reduced, because genetic the algorithm is used to optimize the neural network system.
文摘The performance of deep learning(DL)networks has been increased by elaborating the network structures. However, the DL netowrks have many parameters, which have a lot of influence on the performance of the network. We propose a genetic algorithm(GA) based deep belief neural network(DBNN) method for robot object recognition and grasping purpose. This method optimizes the parameters of the DBNN method, such as the number of hidden units, the number of epochs, and the learning rates, which would reduce the error rate and the network training time of object recognition. After recognizing objects, the robot performs the pick-andplace operations. We build a database of six objects for experimental purpose. Experimental results demonstrate that our method outperforms on the optimized robot object recognition and grasping tasks.
文摘Aimed at the problems of premature and lower convergence of simple genetic algorithms （SGA）, three ideas --partition the whole search uniformly, multi-genetic operators and multi-populations evolving independently are introduced, and a grid-based pseudo-parallel genetic algorithms （GPPGA） is put forward. Thereafter, the analysis of premature and convergence of GPPGA is made. In the end, GPPGA is tested by both six-peak camel back function, Rosenbrock function and BP network. The result shows the feasibility and effectiveness of GPPGA in overcoming premature and improving convergence speed and accuracy.
基金Funded by the High Technology Project(863) of the Ministry of Science and Technology of China(No. 2006AA06A305,6,7)
文摘We developed and tested an improved neural network to predict the average concentration of PM10(particulate matter with diameter smaller than 10 ?m) several hours in advance in summer in Beijing.A genetic algorithm optimization procedure for optimizing initial weights and thresholds of the neural network was also evaluated.This research was based upon the PM10 data from seven monitoring sites in Beijing urban region and meteorological observation data,which were recorded every 3 h during summer of 2002.Two neural network models were developed.Model I was built for predicting PM10 concentrations 3 h in advance while Model II for one day in advance.The predictions of both models were found to be consistent with observations.Percent errors in forecasting the numerical value were about 20.This brings us to the conclusion that short-term fluctuations of PM10 concentrations in Beijing urban region in summer are to a large extent driven by meteorological conditions.Moreover,the predicted results of Model II were compared with the ones provided by the Models-3 Community Multiscale Air Quality(CMAQ) modeling system.The mean relative errors of both models were 0.21 and 0.26,respectively.The performance of the neural network model was similar to numerical models,when applied to short-time prediction of PM10 concentration.
基金Supported by National Outstanding Youth Science Foundation of China(No.41025015)the National Natural Science Foundation of China(No.41274109)Sichuan Youth Science and Technology Innovation Research Team(No.2011JTD0013)
文摘In energy dispersive X-ray fiuorescence(EDXRF), quantitative elemental content analysis becomes difficult due to the existence of the noise, the spectrum peak superposition, element matrix effect, etc. In this paper, a hybrid approach of genetic algorithm(GA) and back propagation(BP) neural network is proposed without considering the complex relationship between the elemental content and peak intensity. The aim of GA-optimized BP is to get better network initial weights and thresholds. The starting point of this approach is that the reciprocal of the mean square error of the initialization BP neural network is set as the fitness value of the individuals in GA; and the initial weights and thresholds are replaced by individuals, then the optimal individual is searched by selecting, crossover and mutation operations, finally a new BP neural network model is established with the optimal initial weights and thresholds. The quantitative analysis results of titanium and iron contents in five types of mineral samples show that the relative errors of 76.7% samples are below 2%, compared to chemical analysis data, which demonstrates the effectiveness of the proposed method.
文摘In this paper, a new neuroevolution algorithm (NEGA) for simultaneous evolution of both architectures and weights of neural networks is described. A whole new network encoding method is shown. The competing conventions problem is solved absolutely. Heuristic methods are used to constrain the topology mutation probability and the trend of mutation kind choice. Also, the niching method is used to protect the network topologies evolution. The experiment results show the efficiency and rapidity of NEGA forcefully.
文摘A real valued genetic algorithm(RVGA) for the optimization problem with continuous variables is proposed. It is composed of a simple and general purpose dynamic scaled fitness and selection operator, crossover operator, mutation operators and adaptive probabilities for these operators. The algorithm is tested by two generally used functions and is used in training a neural network for image recognition. Experimental results show that the algorithm is an efficient global optimization algorithm.
文摘This paper investigated the resistance performance of a submersible surface ship(SSS)in different working cases and scales to analyze the hydrodynamic performance characteristics of an SSS at different speeds and diving depths for engineering applications.First,a hydrostatic resistance performance test of the SSS was carried out in a towing tank.Second,the scale effect of the hydrodynamic pressure coefficient and wave-making resistance was analyzed.The differences between the three-dimensional real-scale ship resistance prediction and numerical methods were explained.Finally,the advantages of genetic algorithm(GA)and neural network were combined to predict the resistance of SSS.Back propagation neural network(BPNN)and GA-BPNN were utilized to predict the SSS resistance.We also studied neural network parameter optimization,including connection weights and thresholds,using K-fold cross-validation.The results showed that when a SSS sails at low and medium speeds,the influence of various underwater cases on resistance is not obvious,while at high speeds,the resistance of water surface cases increases sharply with an increase in speed.After improving the weights and thresholds through K-fold cross-validation and GA,the prediction results of BPNN have high consistency with the actual values.The research results can provide a theoretical reference for the optimal design of the resistance of SSS in practical applications.
文摘A direct feedback control system based on fuzzy recurrent neural network is proposed, and a method of training weights of fuzzy recurrent neural network was designed by applying modified contract mapping genetic algorithm. Computer simulation results indicate that fuzzy recurrent neural network controller has perfect dynamic and static performances .
