The fault diagnosis model for FMS based on multi layer feedforward neural networks was discussed An improved BP algorithm,the tactic of initial value selection based on genetic algorithm and the method of network st...The fault diagnosis model for FMS based on multi layer feedforward neural networks was discussed An improved BP algorithm,the tactic of initial value selection based on genetic algorithm and the method of network structure optimization were presented for training this model ANN(artificial neural network)fault diagnosis model for the robot in FMS was made by the new algorithm The result is superior to the rtaditional algorithm展开更多
A new reliability-based multidisciplinary design optimization （RBMDO） framework is proposed by combining the single-loop-based reliability analysis （SLBRA） method with multidisciplinary feasible （MDF） method. Th...A new reliability-based multidisciplinary design optimization （RBMDO） framework is proposed by combining the single-loop-based reliability analysis （SLBRA） method with multidisciplinary feasible （MDF） method. The Kriging approximate model with updating is introduced to reduce the computational cost of MDF caused by the complex structure. The computational efficiency is remarkably improved as the lack of iterative process during reliability analysis. Special attention is paid to a turbine blade design optimization by adopting the proposed method. Results show that the method is much more efficient than the commonly used double-loop based RBMDO method. It is feasible and efficient to apply the method to the engineering design.展开更多
Deterministic optimization methods are combined with the Pareto front concept to solve multi-criterion design problems. The algorithm and the numerical implementation are applied to aerodynamic designs. Evolutionary a...Deterministic optimization methods are combined with the Pareto front concept to solve multi-criterion design problems. The algorithm and the numerical implementation are applied to aerodynamic designs. Evolutionary algorithms （EAs） and the Pareto front concept are used to solve practical design problems in industry for its robustness in capturing convex, concave, discrete or discontinuous Pareto fronts of multi-objective optimization problems. However, the process is time-consuming. Therefore, deterministic optimization methods are introduced to capture the Pareto front, and the types of the captured Pareto front are explained. Numerical experiments show that the deterministic optimization method is a good alternative to EAs for capturing any convex and some concave Pareto fronts in multi-criterion aerodynamic optimization problems due to its efficiency.展开更多
In order to improve the global search ability of biogeography-based optimization(BBO)algorithm in multi-threshold image segmentation,a multi-threshold image segmentation based on improved BBO algorithm is proposed.Whe...In order to improve the global search ability of biogeography-based optimization(BBO)algorithm in multi-threshold image segmentation,a multi-threshold image segmentation based on improved BBO algorithm is proposed.When using BBO algorithm to optimize threshold,firstly,the elitist selection operator is used to retain the optimal set of solutions.Secondly,a migration strategy based on fusion of good solution and pending solution is introduced to reduce premature convergence and invalid migration of traditional migration operations.Thirdly,to reduce the blindness of traditional mutation operations,a mutation operation through binary computation is created.Then,it is applied to the multi-threshold image segmentation of two-dimensional cross entropy.Finally,this method is used to segment the typical image and compared with two-dimensional multi-threshold segmentation based on particle swarm optimization algorithm and the two-dimensional multi-threshold image segmentation based on standard BBO algorithm.The experimental results show that the method has good convergence stability,it can effectively shorten the time of iteration,and the optimization performance is better than the standard BBO algorithm.展开更多
Optimization under uncertainty is a challenging topic of practical importance in the Process Systems Engineering.Since the solution of an optimization problem generally exhibits high sensitivity to the parameter varia...Optimization under uncertainty is a challenging topic of practical importance in the Process Systems Engineering.Since the solution of an optimization problem generally exhibits high sensitivity to the parameter variations, the deterministic model which neglects the parametric uncertainties is not suitable for practical applications. This paper provides an overview of the key contributions and recent advances in the field of process optimization under uncertainty over the past ten years and discusses their advantages and limitations thoroughly. The discussion is focused on three specific research areas, namely robust optimization, stochastic programming and chance constrained programming, based on which a systematic analysis of their applications, developments and future directions are presented. It shows that the more recent trend has been to integrate different optimization methods to leverage their respective superiority and compensate for their drawbacks. Moreover, data-driven optimization, which combines mathematical programming methods and machine learning algorithms, has become an emerging and competitive tool to handle optimization problems in the presence of uncertainty based on massive historical data.展开更多
In order to solve the core issue of the energy regulation （ER） on multi-energy resource powertrain of fuel cell vehicle, the work functions of each component were defined; the mathematical algorithm model of energy ...In order to solve the core issue of the energy regulation （ER） on multi-energy resource powertrain of fuel cell vehicle, the work functions of each component were defined; the mathematical algorithm model of energy regulation was established and the relevant solution was found. This algorithm was evaluated successfully on the hardware in loop （FILL） platform under three typical urban running cycles. The results showed ER control target had been realized and the mathematical algorithm was effective and reasonable. Based on the HIL simulation, some conclusions and ER strategies were made. According to the different power component parameters and real time control request, this algorithm should be modified and calibrated for application in the actual control system.展开更多
The culture of Magnetospirillum magneticum WM-1 depends on several control factors that have great effect on the magnetic cells concentration. Investigation into the optimal culture conditions needs a large number of ...The culture of Magnetospirillum magneticum WM-1 depends on several control factors that have great effect on the magnetic cells concentration. Investigation into the optimal culture conditions needs a large number of experiments So it is desirable to minimize the number of experiments and maximize the information gained from them. The orthogonal design of experiments and mathematical statistical method are considered as effective methods to optimize the culture condition of magnetotactic bacteria WMol for high magnetic cells concentration. The effects of the four factors, such as pH value of medium, oxygen concentration of gas phase in the serum bottle, C：C （mtartaric acid： m=succinic acid） ratio and NaNO3 concentration, are simultaneously investigated by only sixteen experiments through the orthogonal design L16（44） method. The optimal culture condition is obtained. At the optimal culture condition （ pH 7.0, an oxygen concentration 4.0%, C：C （mtartaric acid： m=succinic acid） ratio 1：2 and NaNO3 100 mg 1^-1）, the magnetic cells concentration is promoted tO 6.5×10^7 cells ml^-1, approximately 8.3% higher than that under the initial conditions. The pH value of medium is a very important factor for magnetic cells concentration. It can be Proved that the orthogonal design of experiment is of 90% confidence. Ferric iron uptake follows MichaelisoMenten kinetics with a Km of 2.5 pM and a Vmax of 0.83 min^-1.展开更多
文摘The fault diagnosis model for FMS based on multi layer feedforward neural networks was discussed An improved BP algorithm,the tactic of initial value selection based on genetic algorithm and the method of network structure optimization were presented for training this model ANN(artificial neural network)fault diagnosis model for the robot in FMS was made by the new algorithm The result is superior to the rtaditional algorithm
基金Supported by the National High Technology Research and Development Program of China("863" Program) (2009AA04Z418, 2007AA04Z404)the National "111" Project(B07050)~~
文摘A new reliability-based multidisciplinary design optimization （RBMDO） framework is proposed by combining the single-loop-based reliability analysis （SLBRA） method with multidisciplinary feasible （MDF） method. The Kriging approximate model with updating is introduced to reduce the computational cost of MDF caused by the complex structure. The computational efficiency is remarkably improved as the lack of iterative process during reliability analysis. Special attention is paid to a turbine blade design optimization by adopting the proposed method. Results show that the method is much more efficient than the commonly used double-loop based RBMDO method. It is feasible and efficient to apply the method to the engineering design.
文摘Deterministic optimization methods are combined with the Pareto front concept to solve multi-criterion design problems. The algorithm and the numerical implementation are applied to aerodynamic designs. Evolutionary algorithms （EAs） and the Pareto front concept are used to solve practical design problems in industry for its robustness in capturing convex, concave, discrete or discontinuous Pareto fronts of multi-objective optimization problems. However, the process is time-consuming. Therefore, deterministic optimization methods are introduced to capture the Pareto front, and the types of the captured Pareto front are explained. Numerical experiments show that the deterministic optimization method is a good alternative to EAs for capturing any convex and some concave Pareto fronts in multi-criterion aerodynamic optimization problems due to its efficiency.
基金Science and Technology Plan of Gansu Province(No.144NKCA040)
文摘In order to improve the global search ability of biogeography-based optimization(BBO)algorithm in multi-threshold image segmentation,a multi-threshold image segmentation based on improved BBO algorithm is proposed.When using BBO algorithm to optimize threshold,firstly,the elitist selection operator is used to retain the optimal set of solutions.Secondly,a migration strategy based on fusion of good solution and pending solution is introduced to reduce premature convergence and invalid migration of traditional migration operations.Thirdly,to reduce the blindness of traditional mutation operations,a mutation operation through binary computation is created.Then,it is applied to the multi-threshold image segmentation of two-dimensional cross entropy.Finally,this method is used to segment the typical image and compared with two-dimensional multi-threshold segmentation based on particle swarm optimization algorithm and the two-dimensional multi-threshold image segmentation based on standard BBO algorithm.The experimental results show that the method has good convergence stability,it can effectively shorten the time of iteration,and the optimization performance is better than the standard BBO algorithm.
文摘Optimization under uncertainty is a challenging topic of practical importance in the Process Systems Engineering.Since the solution of an optimization problem generally exhibits high sensitivity to the parameter variations, the deterministic model which neglects the parametric uncertainties is not suitable for practical applications. This paper provides an overview of the key contributions and recent advances in the field of process optimization under uncertainty over the past ten years and discusses their advantages and limitations thoroughly. The discussion is focused on three specific research areas, namely robust optimization, stochastic programming and chance constrained programming, based on which a systematic analysis of their applications, developments and future directions are presented. It shows that the more recent trend has been to integrate different optimization methods to leverage their respective superiority and compensate for their drawbacks. Moreover, data-driven optimization, which combines mathematical programming methods and machine learning algorithms, has become an emerging and competitive tool to handle optimization problems in the presence of uncertainty based on massive historical data.
基金National High Technology Research and Development Program"863"(No.2001AA501012)
文摘In order to solve the core issue of the energy regulation （ER） on multi-energy resource powertrain of fuel cell vehicle, the work functions of each component were defined; the mathematical algorithm model of energy regulation was established and the relevant solution was found. This algorithm was evaluated successfully on the hardware in loop （FILL） platform under three typical urban running cycles. The results showed ER control target had been realized and the mathematical algorithm was effective and reasonable. Based on the HIL simulation, some conclusions and ER strategies were made. According to the different power component parameters and real time control request, this algorithm should be modified and calibrated for application in the actual control system.
文摘The culture of Magnetospirillum magneticum WM-1 depends on several control factors that have great effect on the magnetic cells concentration. Investigation into the optimal culture conditions needs a large number of experiments So it is desirable to minimize the number of experiments and maximize the information gained from them. The orthogonal design of experiments and mathematical statistical method are considered as effective methods to optimize the culture condition of magnetotactic bacteria WMol for high magnetic cells concentration. The effects of the four factors, such as pH value of medium, oxygen concentration of gas phase in the serum bottle, C：C （mtartaric acid： m=succinic acid） ratio and NaNO3 concentration, are simultaneously investigated by only sixteen experiments through the orthogonal design L16（44） method. The optimal culture condition is obtained. At the optimal culture condition （ pH 7.0, an oxygen concentration 4.0%, C：C （mtartaric acid： m=succinic acid） ratio 1：2 and NaNO3 100 mg 1^-1）, the magnetic cells concentration is promoted tO 6.5×10^7 cells ml^-1, approximately 8.3% higher than that under the initial conditions. The pH value of medium is a very important factor for magnetic cells concentration. It can be Proved that the orthogonal design of experiment is of 90% confidence. Ferric iron uptake follows MichaelisoMenten kinetics with a Km of 2.5 pM and a Vmax of 0.83 min^-1.
