In order to take into account the uncertainties linked to the variables in the evaluation of the statistical properties of structural response, a reliability approach with probabilistic aspect was considered. This is ...In order to take into account the uncertainties linked to the variables in the evaluation of the statistical properties of structural response, a reliability approach with probabilistic aspect was considered. This is called the Probabilistic Transformation Method (PTM). This method is readily applicable when the function between the input and the output of the system is explicit. However, the situation is much more involved when it is necessary to perform the evaluation of implicit function between the input and the output of the system through numerical models. In this work, we propose a technique that combines Finite Element Analysis (FEA) and Probabilistic Transformation Method (PTM) to evaluate the Probability Density Function (PDF) of response where the function between the input and the output of the system is implicit. This technique is based on the numerical simulations of the Finite Element Analysis (FEA) and the Probabilistic Transformation Method (PTM) using an interface between Finite Element software and Matlab. Some problems of structures are treated in order to prove the applicability of the proposed technique. Moreover, the obtained results are compared to those obtained by the reference method of Monte Carlo. A second aim of this work is to develop an algorithm of global optimization using the local method SQP, because of its effectiveness and its rapidity of convergence. For this reason, we have combined the method SQP with the Multi start method. This developed algorithm is tested on test functions comparing with other methods such as the method of Particle Swarm Optimization (PSO). In order to test the applicability of the proposed approach, a structure is optimized under reliability constraints.展开更多
In this paper, we introduce the concept of a (weak) minimizer of order k for a nonsmooth vector optimization problem over cones. Generalized classes of higher-order cone-nonsmooth (F, ρ)-convex functions are introduc...In this paper, we introduce the concept of a (weak) minimizer of order k for a nonsmooth vector optimization problem over cones. Generalized classes of higher-order cone-nonsmooth (F, ρ)-convex functions are introduced and sufficient optimality results are proved involving these classes. Also, a unified dual is associated with the considered primal problem, and weak and strong duality results are established.展开更多
There are several problems existing in the direct starting of asynchronous motor such as large starting current,reactive power absorption from network side and weak interference-resistance,etc.Aiming at this,a compreh...There are several problems existing in the direct starting of asynchronous motor such as large starting current,reactive power absorption from network side and weak interference-resistance,etc.Aiming at this,a comprehensive energy-saving optimization model of asynchronous motor for voltage regulation based on static synchronous compensator(STATCOM)is put forward.By analyzing the working principle and operation performance of static synchronous compensator regulating voltage,a new energy-efficient optimization method for asynchronous motor is proposed based on the voltage regulator model to achieve soft start,continuous dynamic reactive power compensation and the terminal voltage stability control.The multi-objective optimal operation of asynchronous motor is realized by controlling the inverter to adjust the reactive current dynamically.The strategy reduces the influence of starting current and grid voltage by soft starting,and realizes the function of reactive power compensation and terminal voltage stabilization.The effectiveness and superiority of the proposed model is verified by the simulation analysis and the results of comparison with the motor started directly.展开更多
The tokamak start-up is a very important phase during the process to obtain a suitable equalizing plasma, and its governing model can be described as a set of nonlinear ordinary differential equations（ODEs）. In this...The tokamak start-up is a very important phase during the process to obtain a suitable equalizing plasma, and its governing model can be described as a set of nonlinear ordinary differential equations（ODEs）. In this paper, we first estimate the parameters in the original model and set up an accurate model to express how the variables change during the start-up phase, especially how the plasma current changes with respect to time and the loop voltage. Then, we apply the control parameterization method to obtain an approximate optimal parameters selection problem for the loop voltage design to achieve a desired plasma current target. Computational optimal control techniques such as the variational method and the costate method are employed to solve the problem, respectively. Finally, numerical simulations are performed and the results obtained via different methods are compared. Our numerical parameterization method and optimization procedure turn out to be effective.展开更多
Classifying the data into a meaningful group is one of the fundamental ways of understanding and learning the valuable information. High-quality clustering methods are necessary for the valuable and efficient analysis...Classifying the data into a meaningful group is one of the fundamental ways of understanding and learning the valuable information. High-quality clustering methods are necessary for the valuable and efficient analysis of the increasing data. The Firefly Algorithm (FA) is one of the bio-inspired algorithms and it is recently used to solve the clustering problems. In this paper, Hybrid F-Firefly algorithm is developed by combining the Fuzzy C-Means (FCM) with FA to improve the clustering accuracy with global optimum solution. The Hybrid F-Firefly algorithm is developed by incorporating FCM operator at the end of each iteration in FA algorithm. This proposed algorithm is designed to utilize the goodness of existing algorithm and to enhance the original FA algorithm by solving the shortcomings in the FCM algorithm like the trapping in local optima and sensitive to initial seed points. In this research work, the Hybrid F-Firefly algorithm is implemented and experimentally tested for various performance measures under six different benchmark datasets. From the experimental results, it is observed that the Hybrid F-Firefly algorithm significantly improves the intra-cluster distance when compared with the existing algorithms like K-means, FCM and FA algorithm.展开更多
针对海量数据提出一种基于改进Fisher分数(F-score)特征选择的改进粒子群优化的BP(Modified Particle Swarm Optimization and Back Propagation,MPSO-BP)神经网络短期负荷预测方法。首先采用改进F-score特征评价准则计算影响负荷预测...针对海量数据提出一种基于改进Fisher分数(F-score)特征选择的改进粒子群优化的BP(Modified Particle Swarm Optimization and Back Propagation,MPSO-BP)神经网络短期负荷预测方法。首先采用改进F-score特征评价准则计算影响负荷预测精度各个特征的F-score值,再通过F-score Area法设定阈值筛选出最优特征子集,然后将最优特征子集作为MPSO-BP神经网络模型的输入变量完成对预测日一天24点负荷的预测,并与MPSO-BP神经网络短期负荷预测和传统BP神经网络短期负荷预测进行对比。算例表明,文中提出的短期负荷预测方法可以较好地对海量数据进行挖掘,具有较高的预测精度。展开更多
文摘In order to take into account the uncertainties linked to the variables in the evaluation of the statistical properties of structural response, a reliability approach with probabilistic aspect was considered. This is called the Probabilistic Transformation Method (PTM). This method is readily applicable when the function between the input and the output of the system is explicit. However, the situation is much more involved when it is necessary to perform the evaluation of implicit function between the input and the output of the system through numerical models. In this work, we propose a technique that combines Finite Element Analysis (FEA) and Probabilistic Transformation Method (PTM) to evaluate the Probability Density Function (PDF) of response where the function between the input and the output of the system is implicit. This technique is based on the numerical simulations of the Finite Element Analysis (FEA) and the Probabilistic Transformation Method (PTM) using an interface between Finite Element software and Matlab. Some problems of structures are treated in order to prove the applicability of the proposed technique. Moreover, the obtained results are compared to those obtained by the reference method of Monte Carlo. A second aim of this work is to develop an algorithm of global optimization using the local method SQP, because of its effectiveness and its rapidity of convergence. For this reason, we have combined the method SQP with the Multi start method. This developed algorithm is tested on test functions comparing with other methods such as the method of Particle Swarm Optimization (PSO). In order to test the applicability of the proposed approach, a structure is optimized under reliability constraints.
文摘In this paper, we introduce the concept of a (weak) minimizer of order k for a nonsmooth vector optimization problem over cones. Generalized classes of higher-order cone-nonsmooth (F, ρ)-convex functions are introduced and sufficient optimality results are proved involving these classes. Also, a unified dual is associated with the considered primal problem, and weak and strong duality results are established.
文摘There are several problems existing in the direct starting of asynchronous motor such as large starting current,reactive power absorption from network side and weak interference-resistance,etc.Aiming at this,a comprehensive energy-saving optimization model of asynchronous motor for voltage regulation based on static synchronous compensator(STATCOM)is put forward.By analyzing the working principle and operation performance of static synchronous compensator regulating voltage,a new energy-efficient optimization method for asynchronous motor is proposed based on the voltage regulator model to achieve soft start,continuous dynamic reactive power compensation and the terminal voltage stability control.The multi-objective optimal operation of asynchronous motor is realized by controlling the inverter to adjust the reactive current dynamically.The strategy reduces the influence of starting current and grid voltage by soft starting,and realizes the function of reactive power compensation and terminal voltage stabilization.The effectiveness and superiority of the proposed model is verified by the simulation analysis and the results of comparison with the motor started directly.
基金supported by the National Natural Science Foundation of China(Grant Nos.61104048 and 61473253)the National High Technology Research and Development Program of China(Grant No.2012AA041701)
文摘The tokamak start-up is a very important phase during the process to obtain a suitable equalizing plasma, and its governing model can be described as a set of nonlinear ordinary differential equations（ODEs）. In this paper, we first estimate the parameters in the original model and set up an accurate model to express how the variables change during the start-up phase, especially how the plasma current changes with respect to time and the loop voltage. Then, we apply the control parameterization method to obtain an approximate optimal parameters selection problem for the loop voltage design to achieve a desired plasma current target. Computational optimal control techniques such as the variational method and the costate method are employed to solve the problem, respectively. Finally, numerical simulations are performed and the results obtained via different methods are compared. Our numerical parameterization method and optimization procedure turn out to be effective.
文摘Classifying the data into a meaningful group is one of the fundamental ways of understanding and learning the valuable information. High-quality clustering methods are necessary for the valuable and efficient analysis of the increasing data. The Firefly Algorithm (FA) is one of the bio-inspired algorithms and it is recently used to solve the clustering problems. In this paper, Hybrid F-Firefly algorithm is developed by combining the Fuzzy C-Means (FCM) with FA to improve the clustering accuracy with global optimum solution. The Hybrid F-Firefly algorithm is developed by incorporating FCM operator at the end of each iteration in FA algorithm. This proposed algorithm is designed to utilize the goodness of existing algorithm and to enhance the original FA algorithm by solving the shortcomings in the FCM algorithm like the trapping in local optima and sensitive to initial seed points. In this research work, the Hybrid F-Firefly algorithm is implemented and experimentally tested for various performance measures under six different benchmark datasets. From the experimental results, it is observed that the Hybrid F-Firefly algorithm significantly improves the intra-cluster distance when compared with the existing algorithms like K-means, FCM and FA algorithm.
文摘针对海量数据提出一种基于改进Fisher分数(F-score)特征选择的改进粒子群优化的BP(Modified Particle Swarm Optimization and Back Propagation,MPSO-BP)神经网络短期负荷预测方法。首先采用改进F-score特征评价准则计算影响负荷预测精度各个特征的F-score值,再通过F-score Area法设定阈值筛选出最优特征子集,然后将最优特征子集作为MPSO-BP神经网络模型的输入变量完成对预测日一天24点负荷的预测,并与MPSO-BP神经网络短期负荷预测和传统BP神经网络短期负荷预测进行对比。算例表明,文中提出的短期负荷预测方法可以较好地对海量数据进行挖掘,具有较高的预测精度。
