查询词典 topology structure

Micro flexible mechanical structure topology optimization homogenization theory adjoint operator sensitivity analysis

微小型柔性结构；拓扑优化；均匀化理论；伴随变量法；敏度分析

The development of perimeter control of the checkerboard involved in structure topology optimization of multiphase materials is presented.

针对多相材料结构的拓扑优化出现的棋盘格问题，研究了周长约束控制方法。

Topology optimization aims at finding the optimal distribution of materials in a prescribed design domain and the optimal way of component connection in a discrete structure.

所谓拓扑优化，就是要在给定的约束条件下，在一块预先给定的设计区域中决定最优的材料分布方式或者构件之间的最优连接关系。

Based upon all above-mentioned, a mathematical representation for multi-material structures is proposed by means of vector level sets, and the general structure topology optimization can be expressed by a constrained functional minimization problem of a set of level set functions.

同时，还提出了在有效约束切空间上构造的非线性映射技术，以改进向量水平集的演化方向，可提高计算效率2到3倍。

Using level-set method, mathematical representation for contimuum structures is proposed by means of the vector of level-set, and the general structure topology optimization can be expressed by a constrained functional minimization problem of a set of level set functions.

其次利用水平集方法将一般拓扑优化问题描述为一组水平集函数的约束泛函极小化问题，应用敏度分析，给出了此泛函极小化数值迭代求解公式，即水平集演化方程。

Based on the new penal function, OC algorithm are deduced and used for the topology optimization problem of continuum structure.

基于新的惩罚函数，推导并应用了适用于大型结构、多设计变量的准则法。

Thecharacteristic and different using limit of both optimization criteria method andmathematics programming method are all analyzed in this paper. OC algorithm and theseries MMA algorithm based on SIMP approach are deducedand used for the topology optimization problem of continuum structure. MMA algorithm hasa bad convergence but it has a high computational efficiency, while GCMMA is excellent onconvergence and stability with bad calculation efficiency.

优化求解算法是拓扑优化问题求解的核心和关键，本文系统分析了适用于离散结构和连续体结构优化的优化求解算法，重点讨论了 OC 算法和 MP 算法，分析比较了不同算法的特点和适用范围，推导了基于 SIMP 理论的优化准则法和 MMA 系列算法，并将MMA 系列算法应用于连续体结构拓扑优化问题的求解。

The research of this paper is began with the analysis the geographic spatial character of the UPTN, and designs the UPTN data mode based on GIS. Then, based on the good exhibiting all kinds of topology of the UPTN, and combined with the technology evaluation standards of the UPTN, this paper builds the UPTN planning mathematic model, and puts forward the quantitative calculation of the UPTN evaluation standards based on GIS; brings forward the arithmetic of the optimal path of the UPTN based on the public traffic network storage structure; designs the bus lines grouping management model, and realizes the contradistinctive evaluation of the UPTN planning projects based on GIS.

论文研究的重点内容是从分析城市公交网络中地理空间实体的特征开始，设计基于GIS的城市公交网络数据模型，在有效表现公交网络拓扑关系的基础上，根据公交线网技术评价指标，建立基于GIS的公交线网规划的数学模型，并给出公交线网评价指标的GIS量化计算方法；提出以公交线网数据存储结构为基础的公交网络最优路径算法；设计公交线路分组管理模型，实现公交线网规划方案对比分析。

The relationship between the topology and convegence of networks is approached and the reason causing overtraining and overfitting is analysed. Then the redundant weights and nodes in the network are defined by using improved activity function and a new algorithm called the BPNSO for determining the network structure for any application is proposed, which use dual optimization objective function combining the mean squre error of the network output with network complexity penalty function.

文章对网络拓扑结构与网络收敛性和稳定性的关系进行了探讨，分析了网络出现Overtraining或Overfitting的原因和解决的目标，提出了神经网络结构优化的策略，改进了活化函数，定义了冗余权值和冗余节点的概念，为辨识和删除冗余权值和冗余节点，提出采用优化目标函数中增加网络复杂度罚函数的双重优化目标函数，并对罚因子提出了基于学习误差和权值的自适应动态调整公式，论文定义了这种BP网络结构优化方法为BPNSO，并给出了完整的计算流程。

The network structure is significant in characterizing the performance of the feedforward network, such as network capacity, learning speed, generalization ability Standard back propagation performs gradient descent only in the weight space of a network with fixed topology, in general, it is useful only when the network architecture is chosen correctly With a bad structure we potentially run into problems like underfitting, overfitting or wasting computational resources, so algorithms than can find an appropriate network architecture automatically are highly attractive.

神经网络的结构对于前向网络的特性有重要的影响，如网络容量，学习速度，泛化能力等。标准BP算法是针对网络结构是固定的情况下搜索权值，一般来说，这只能在网络结构被正确选取才有用。如果网络结构不当，我们可能会欠拟合、过拟合或浪费计算资源，因此如果能找到能自动选取网络结构的算法是很有吸引力的。

The split between the two groups can hardly be papered over.

这两个团体间的分歧难以掩饰。

This approach not only encourages a greater number of responses, but minimizes the likelihood of stale groupthink.

这种做法不仅鼓励了更多的反应，而且减少跟风的可能性。