The relationship between the technique by state- dependent Riccati equations （SDRE） and Hamilton-Jacobi-lsaacs （HJI） equations for nonlinear H∞ control design is investigated. By establishing the Lyapunov matrix ...The relationship between the technique by state- dependent Riccati equations （SDRE） and Hamilton-Jacobi-lsaacs （HJI） equations for nonlinear H∞ control design is investigated. By establishing the Lyapunov matrix equations for partial derivates of the solution of the SDREs and introducing symmetry measure for some related matrices, a method is proposed for examining whether the SDRE method admits a global optimal control equiva- lent to that solved by the HJI equation method. Two examples with simulation are given to illustrate the method is effective.展开更多
To improve the global search ability and imaging quality of electrical resistivity imaging(ERI) inversion, a two-stage learning ICPSO algorithm of radial basis function neural network(RBFNN) based on information crite...To improve the global search ability and imaging quality of electrical resistivity imaging(ERI) inversion, a two-stage learning ICPSO algorithm of radial basis function neural network(RBFNN) based on information criterion(IC) and particle swarm optimization(PSO) is presented. In the proposed method, IC is applied to obtain the hidden layer structure by calculating the optimal IC value automatically and PSO algorithm is used to optimize the centers and widths of the radial basis functions in the hidden layer. Meanwhile, impacts of different information criteria to the inversion results are compared, and an implementation of the proposed ICPSO algorithm is given. The optimized neural network has one hidden layer with 261 nodes selected by AKAIKE's information criterion(AIC) and it is trained on 32 data sets and tested on another 8 synthetic data sets. Two complex synthetic examples are used to verify the feasibility and effectiveness of the proposed method with two learning stages. The results show that the proposed method has better performance and higher imaging quality than three-layer and four-layer back propagation neural networks(BPNNs) and traditional least square(LS) inversion.展开更多
间接边界积分方程法IBIEM(indirect boundary integral equation method)求解波动问题时控制方程基本解构造依赖经验判断和试算,导致宽频散射求解不够稳定。本文通过粒子群优化-人工神经网络建立IBIEM控制方程基本解构造模型,以数据驱...间接边界积分方程法IBIEM(indirect boundary integral equation method)求解波动问题时控制方程基本解构造依赖经验判断和试算,导致宽频散射求解不够稳定。本文通过粒子群优化-人工神经网络建立IBIEM控制方程基本解构造模型,以数据驱动代替经验判断,处理基本解构造过程中的不确定性。以二维峡谷对平面SH波散射IBIEM模拟为例验证所建模型的可靠性。结果表明,所建IBIEM控制方程基本解构造模型可对虚拟波源位置和数量的最优设置进行有效预测,兼顾计算效率和精度,大幅提高IBIEM求解波动问题时的稳定性和高效性;虚拟波源位置和数量最优设置方案受入射波频率和场地几何条件影响显著,且表现出非单调变化特征,依据经验设置基本解可靠性较差,以数据驱动的预测模型具有明显优势。本文所建方法可为IBIEM求解其他类型场地地震波动问题提供参考。展开更多
基金supported by the National Natural Science Foundation of China(60874114)
文摘The relationship between the technique by state- dependent Riccati equations （SDRE） and Hamilton-Jacobi-lsaacs （HJI） equations for nonlinear H∞ control design is investigated. By establishing the Lyapunov matrix equations for partial derivates of the solution of the SDREs and introducing symmetry measure for some related matrices, a method is proposed for examining whether the SDRE method admits a global optimal control equiva- lent to that solved by the HJI equation method. Two examples with simulation are given to illustrate the method is effective.
基金Project(41374118)supported by the National Natural Science Foundation,ChinaProject(20120162110015)supported by Research Fund for the Doctoral Program of Higher Education,China+3 种基金Project(2015M580700)supported by the China Postdoctoral Science Foundation,ChinaProject(2016JJ3086)supported by the Hunan Provincial Natural Science Foundation,ChinaProject(2015JC3067)supported by the Hunan Provincial Science and Technology Program,ChinaProject(15B138)supported by the Scientific Research Fund of Hunan Provincial Education Department,China
文摘To improve the global search ability and imaging quality of electrical resistivity imaging(ERI) inversion, a two-stage learning ICPSO algorithm of radial basis function neural network(RBFNN) based on information criterion(IC) and particle swarm optimization(PSO) is presented. In the proposed method, IC is applied to obtain the hidden layer structure by calculating the optimal IC value automatically and PSO algorithm is used to optimize the centers and widths of the radial basis functions in the hidden layer. Meanwhile, impacts of different information criteria to the inversion results are compared, and an implementation of the proposed ICPSO algorithm is given. The optimized neural network has one hidden layer with 261 nodes selected by AKAIKE's information criterion(AIC) and it is trained on 32 data sets and tested on another 8 synthetic data sets. Two complex synthetic examples are used to verify the feasibility and effectiveness of the proposed method with two learning stages. The results show that the proposed method has better performance and higher imaging quality than three-layer and four-layer back propagation neural networks(BPNNs) and traditional least square(LS) inversion.
文摘间接边界积分方程法IBIEM(indirect boundary integral equation method)求解波动问题时控制方程基本解构造依赖经验判断和试算,导致宽频散射求解不够稳定。本文通过粒子群优化-人工神经网络建立IBIEM控制方程基本解构造模型,以数据驱动代替经验判断,处理基本解构造过程中的不确定性。以二维峡谷对平面SH波散射IBIEM模拟为例验证所建模型的可靠性。结果表明,所建IBIEM控制方程基本解构造模型可对虚拟波源位置和数量的最优设置进行有效预测,兼顾计算效率和精度,大幅提高IBIEM求解波动问题时的稳定性和高效性;虚拟波源位置和数量最优设置方案受入射波频率和场地几何条件影响显著,且表现出非单调变化特征,依据经验设置基本解可靠性较差,以数据驱动的预测模型具有明显优势。本文所建方法可为IBIEM求解其他类型场地地震波动问题提供参考。
