叠前反演是获取地下介质弹性参数的一种重要手段,马尔可夫蒙特卡洛(Markov Chain Monte Carlo,MCMC)算法是叠前反演求解的经典方法。相比于传统的数值优化算法和线性反演方法,MCMC反演算法具备更高的精度,但仍然存在依赖初始模型、计算...叠前反演是获取地下介质弹性参数的一种重要手段,马尔可夫蒙特卡洛(Markov Chain Monte Carlo,MCMC)算法是叠前反演求解的经典方法。相比于传统的数值优化算法和线性反演方法,MCMC反演算法具备更高的精度,但仍然存在依赖初始模型、计算耗时长和不确定性大等问题。为此,对常规MCMC反演算法进行改进,提出基于构造倾角约束的BLI-MCMC叠前随机反演方法。首先,将地质构造倾角加入先验约束信息中,提高反演的采样效率,降低反演结果的不确定性;然后,利用贝叶斯线性反演(Bayesian Linear Inversion,BLI)算法为MCMC反演提供良好的初始模型,并作为迭代起点,缩短马尔科夫链的燃烧时间,从初始模型角度提高反演的效率。模拟数据和实际资料应用结果均表明,改进后的方法能够显著提高反演精度和效率,保持了地下介质较高的横向连续性。该方法可为地下起伏介质的反演提供技术支撑。展开更多
An improved genetic algorithm（IGA） based on a novel selection strategy to handle nonlinear programming problems is proposed.Each individual in selection process is represented as a three-dimensional feature vector w...An improved genetic algorithm（IGA） based on a novel selection strategy to handle nonlinear programming problems is proposed.Each individual in selection process is represented as a three-dimensional feature vector which is composed of objective function value,the degree of constraints violations and the number of constraints violations.It is easy to distinguish excellent individuals from general individuals by using an individuals＇ feature vector.Additionally,a local search（LS） process is incorporated into selection operation so as to find feasible solutions located in the neighboring areas of some infeasible solutions.The combination of IGA and LS should offer the advantage of both the quality of solutions and diversity of solutions.Experimental results over a set of benchmark problems demonstrate that IGA has better performance than other algorithms.展开更多
A constrained generalized predictive control （GPC） algorithm based on the T-S fuzzy model is presented for the nonlinear system. First, a Takagi-Sugeno （T-S） fuzzy model based on the fuzzy cluster algorithm and th...A constrained generalized predictive control （GPC） algorithm based on the T-S fuzzy model is presented for the nonlinear system. First, a Takagi-Sugeno （T-S） fuzzy model based on the fuzzy cluster algorithm and the orthogonalleast square method is constructed to approach the nonlinear system. Since its consequence is linear, it can divide the nonlinear system into a number of linear or nearly linear subsystems. For this T-S fuzzy model, a GPC algorithm with input constraints is presented. This strategy takes into account all the constraints of the control signal and its increment, and does not require the calculation of the Diophantine equations. So it needs only a small computer memory and the computational speed is high. The simulation results show a good performance for the nonlinear systems.展开更多
The receding horizon control（RHC） problem is considered for nonlinear Markov jump systems which can be represented by Takagi-Sugeno fuzzy models subject to constraints both on control inputs and on observe outputs.I...The receding horizon control（RHC） problem is considered for nonlinear Markov jump systems which can be represented by Takagi-Sugeno fuzzy models subject to constraints both on control inputs and on observe outputs.In the given receding horizon,for each mode sequence of the T-S modeled nonlinear system with Markov jump parameter,the cost function is optimized by constraints on state trajectories,so that the optimization control input sequences are obtained in order to make the state into a terminal invariant set.Out of the receding horizon,the stability is guaranteed by searching a state feedback control law.Based on such stability analysis,a linear matrix inequality approach for designing receding horizon predictive controller for nonlinear systems subject to constraints both on the inputs and on the outputs is developed.The simulation shows the validity of this method.展开更多
文摘叠前反演是获取地下介质弹性参数的一种重要手段,马尔可夫蒙特卡洛(Markov Chain Monte Carlo,MCMC)算法是叠前反演求解的经典方法。相比于传统的数值优化算法和线性反演方法,MCMC反演算法具备更高的精度,但仍然存在依赖初始模型、计算耗时长和不确定性大等问题。为此,对常规MCMC反演算法进行改进,提出基于构造倾角约束的BLI-MCMC叠前随机反演方法。首先,将地质构造倾角加入先验约束信息中,提高反演的采样效率,降低反演结果的不确定性;然后,利用贝叶斯线性反演(Bayesian Linear Inversion,BLI)算法为MCMC反演提供良好的初始模型,并作为迭代起点,缩短马尔科夫链的燃烧时间,从初始模型角度提高反演的效率。模拟数据和实际资料应用结果均表明,改进后的方法能够显著提高反演精度和效率,保持了地下介质较高的横向连续性。该方法可为地下起伏介质的反演提供技术支撑。
基金supported by the National Natural Science Foundation of China (60632050)National Basic Research Program of Jiangsu Province University (08KJB520003)
文摘An improved genetic algorithm（IGA） based on a novel selection strategy to handle nonlinear programming problems is proposed.Each individual in selection process is represented as a three-dimensional feature vector which is composed of objective function value,the degree of constraints violations and the number of constraints violations.It is easy to distinguish excellent individuals from general individuals by using an individuals＇ feature vector.Additionally,a local search（LS） process is incorporated into selection operation so as to find feasible solutions located in the neighboring areas of some infeasible solutions.The combination of IGA and LS should offer the advantage of both the quality of solutions and diversity of solutions.Experimental results over a set of benchmark problems demonstrate that IGA has better performance than other algorithms.
基金This Project was supported by the National Natural Science Foundation of China (60374037 and 60574036)the Opening Project Foundation of National Lab of Industrial Control Technology (0708008).
文摘A constrained generalized predictive control （GPC） algorithm based on the T-S fuzzy model is presented for the nonlinear system. First, a Takagi-Sugeno （T-S） fuzzy model based on the fuzzy cluster algorithm and the orthogonalleast square method is constructed to approach the nonlinear system. Since its consequence is linear, it can divide the nonlinear system into a number of linear or nearly linear subsystems. For this T-S fuzzy model, a GPC algorithm with input constraints is presented. This strategy takes into account all the constraints of the control signal and its increment, and does not require the calculation of the Diophantine equations. So it needs only a small computer memory and the computational speed is high. The simulation results show a good performance for the nonlinear systems.
基金supported by the National Natural Science Foundation of China (6097400160904045)+1 种基金National Natural Science Foundation of Jiangsu Province (BK2009068)Six Projects Sponsoring Talent Summits of Jiangsu Province
文摘The receding horizon control（RHC） problem is considered for nonlinear Markov jump systems which can be represented by Takagi-Sugeno fuzzy models subject to constraints both on control inputs and on observe outputs.In the given receding horizon,for each mode sequence of the T-S modeled nonlinear system with Markov jump parameter,the cost function is optimized by constraints on state trajectories,so that the optimization control input sequences are obtained in order to make the state into a terminal invariant set.Out of the receding horizon,the stability is guaranteed by searching a state feedback control law.Based on such stability analysis,a linear matrix inequality approach for designing receding horizon predictive controller for nonlinear systems subject to constraints both on the inputs and on the outputs is developed.The simulation shows the validity of this method.
