可视化约束程序编程环境的设计与实现

该文提出了一种应用于多数化CAD系统的面向约束的程序设计思想,开发了一个实用的可视化约束程序编程环境。不仅简化了用户在参数化图形设计过程中大量约束程序的编程,而且提出了一种应用于参数化CAD系统设计图形约束程序的可视化方法。     

谈AUTOCAD参数化绘图的应用

以往在利用CAD绘图的过程中,必须要先分析图形的图元组成,然后绘制出图元,最后进行编辑及标注完成绘图,基于约束的参数化绘图则大大的提高了绘图的效率,它让用户通过基于设计意图和最终的图形的对象约束绘制出图形。     

语义特征模型中参数范围的计算

基于参数和语义特征的CAD系统,改变参数的值是最重要的操作,也通常是一种反复性的过程,针对适合的参数值对于用户来说提前是不知道的,提出一种新的方法用于自动侦测参数的适当的范围。先通过分析拓扑约束图,找到距离和角度约束,并将这些约束加入到其相应的几何约束中,然后通过分解找到子问题的临界参数值,再求解每个并列的参数区间内部的一个具体的实例,精确的参数范围就能够被确定。     

一种基于功能约束的参数化图形设计方法

本文在现有参数化图形设计方法的基础上述通过对设计过程中功能要求的研究，提出了功能约束的概念，把功能约束引入参数化图形设计过程。在实践的基础邮基于功能约束的参数化图形设计的三种算法，并给出了部分实践的结果。采用本文提出的方法，较好地实现了产品设计中从功能得结构的设计集成，为进一步研究设计集成问题提供了一种尝试。     

参数化CAD中参数有效范围的算法研究

确定一类二维参数化CAD模型中参数的有效范围,可减少在参数化CAD系统中重建几何实体失败的情况,为此提出了相应的代数算法。所有简单多边形中距离约束参数的有效值取值范围均可以通过此算法求出,但是求解效率不高。通过多次计算验证得出无论在有效取值范围内的任一赋值均可使重建后的几何实体的拓扑形状不变,提高参数化CAD软件的设计效率和人机交互的智能化水平,并分析出该算法的复杂度为O(n)2。     

PADS──一个基于几何推理的参数化设计系统

ＰＡＤＳ是一个基于几何推理的参数化设计系统。该系统采用一面向对象的数据模型统一表示几何元素与几何约束；基于一个普通算法实现几何推理，并且推理算法采用了一个更加适合于几何推理的推理策略；系统的参数变动处理通过局部的几何推理，将尺寸变动后的重新计算限制在局部范围；为减轻用户的输入负担，系统具备几种有效的输入与建模手段。     

基于拓展LIMD算法的智能动态几何软件设计

几何约束求解技术是新一代智能化参数化CAD的核心技术之一,是CAD领域的一个前沿课题．其目的是提供工程图形的自动求解,其主要特点是：自由拖动元素、动态图形生成、动态测量、动态轨迹生成．LIMD是约束求解中一个应用较广的算法,作者对该算法进行了拓宽和改进,提出了具体解决方法,并得到了较好结果．     

基于约束的参数化驱动模型研究

参数化设计在现代CAD系统中占据着越来越重要的地位 ,参数驱动过程实质就是几何约束的满足和求解过程。针对传统CAD系统中存在的不足之处 ,本文主要讨论了基于约束的参数驱动模型的实现原理 ,几何约束的表达和求解方法 ,以及几何约束和工程约束的关联关系。概括了参数化绘图系统的主要特征。     

参数化CAD中参数的有效范围

在参数化CAD设计中，当重新生成一个几何实体时，常常由于所给的参数值不合理而导致重新生成的几何实体的拓扑形状发生改变，有时甚至无法重新生成几何实体．提出确定某类二维参数化CAD模型中参数的有效范围的代数算法．该算法的复杂度是O(n^2logn)．     

基于采矿CAD的硐室参数绘图系统设计及实现

CAD系统因其本身具有许多长处,得到了工程设计人员的广泛使用。但它只能处理图形的几何信息,真正具有工程实际意义的图形拓扑信息和参数约束信息均被抛弃了。为了保留更多的图形信息以及让工程设计人员更方便地进行硐室图形的绘制,文中根据采矿CAD图形的特点,把要绘制的硐室图形进行参数化分析,并通过编程调用采矿CAD的接口实现了硐室图形的自动绘制系统。此系统能根据用户输入的参数自动生成硐室的二维和三维图形,这大大减少了设计人员的工作量,提高了设计效率,也有利于计算机辅助设计的进一步发展。     

基于仿真和知识的广义动态约束网络技术

针对并行协同设计中的参数不确定性,将普通的约束网络扩展为广义动态约束网络,以对设计中的不确定性信息进行管理.建立了包含领域级约束和知识级约束的广义动态约束网络模型;提出了基于仿真分析和自适应响应面法的领域级约束建模的有效方法,并提出模糊-粗糙集算法,对仿真结果进行数据挖掘,实现了知识级约束获取;基于模板技术给出了广义动态约束网络中各种约束的统一表示方法;构造了有效的约束冲突求解策略和一致性求解算法,求出一致性设计区间.最后通过设计实例验证了文中方法的有效性.     

Constrained parameter estimation with applications to blending operations

The classical least squares approach to parameter estimation for dynamic models ignores a priori information about the feasible values of the estimated parameters. But in many practical problems, such information is available in the form of upper and lower limits. In this paper, two alternative techniques are evaluated for this important class of constrained parameter estimation problems for dynamic systems. Simulation results for two blending problems illustrate that more accurate parameter estimates and better predictions can be obtained by using a quadratic programming approach.     

基于几何推理的布局设计新方法

面向约束的布局设计问题一直是布局研究的热点．本文提出了一个基于几何推理的布局设计生成算法，其最大优点就是充分利用参数化设计的优点，建立了影响布局设计的变量之间的关系．该算法具有较强的柔性，易于扩充，并支持约束一致性检测和影响布局设计参变量的局部修改．算法已在作者研制的车身内布置设计系统中得到了较好的应用．     

Nonlinear adaptive control of robotic manipulators - hyperstability approach

A nonlinear model reference adaptive controller based on hyperstability approach, is presented for the control of robot manipulators. Use of hyperstability approach simplifies the stability proof of the adaptive system. The unknown parameters of the system, as well as its variable payload, are estimated on line and are adaptive to their actual values; tending to reduce the system error. In addition, any sudden change in the system parameters or payload is detected by the proposed intelligent controller. Robot path tracking, with unknown parameter values and variable payload, is simulated to show the effectiveness of the proposed adaptive control algorithm. Both system output error and parameter estimation error vanish under the proposed parameter adaptation algorithm.     

蚁群聚类组合方法参数M的研究

蚁群算法中参数在不同取值情况下,常常会对算法的性能和求解效率产生重大影响。该文在基于蚁群聚类组合方法的研究基础上,重点研究了蚁群聚类组合方法KMAOC算法中蚁群算法参数蚂蚁数m对KMAOC算法性能的影响,对KMAOC算法中的参数蚂蚁数m分别取值进行实验,通过几组实验验证提供了KMAOC算法中参数蚂蚁数m配置的较好建议。     

Gradient‐based back‐propagation dynamical iterative learning scheme for the neuro‐fuzzy inference system

In this paper, a gradient‐based back propagation dynamical iterative learning algorithm is proposed for structure optimization and parameter tuning of the neuro‐fuzzy system. Premise and consequent parameters of the neuro‐fuzzy model are initialized randomly and then tuned by the proposed iterative algorithm. The learning algorithm is based on the first order partial derivative of the output with respect to the structure parameters. The first order derivative of the model output with respect to the structure parameters determines the sensitivity of the model to structure parameters. The sensitivity values are then used to set the tuning factors and parameters updating step sizes. Therefore, an adaptive dynamical iterative scheme is achieved which adapts the learning procedure to the current state of the performance during the optimization process. Larger tuning step sizes make the convergence speed higher and vice versa. In this regard, this parameter is treated according to the calculated sensitivity of the model to the parameter. The proposed learning algorithm is compared with the least square back propagation method, genetic algorithm and chaotic genetic algorithm in the neuro‐fuzzy model structure optimization. Smaller mean square error and shorter learning time are sought in this paper, and the performance of the proposed learning algorithm is versified regarding these criteria.     

Levy distributed parameter control in differential evolution for numerical optimization

Differential evolution (DE) algorithm is a population based stochastic search technique widely applied in scientific and engineering fields for global optimization over real parameter space. The performance of DE algorithm highly depends on the selection of values of the associated control parameters. Therefore, finding suitable values of control parameters is a challenging task and researchers have already proposed several adaptive and self-adaptive variants of DE. In the paper control parameters are adapted by levy distribution, named as Levy distributed DE (LdDE) which efficiently handles exploration and exploitation dilemma in the search space. In order to assure a fair comparison with existing parameter controlled DE algorithms, we apply the proposed method on number of well-known unimodal, basic and expanded multimodal and hybrid composite benchmark optimization functions having different dimensions. The empirical study shows that the proposed LdDE algorithm exhibits an overall better performance in terms of accuracy and convergence speed compared to five prominent adaptive DE algorithms.     

State estimation and parameter identification method for dual-rate system based on improved Kalman prediction

For the dual-rate system, such as the process of space teleoperation whose control signals is partly determined by delayed feedback states, the state values and system parameters are coupled and influenced each other, which are hard to be estimated simultaneously. In this paper, we propose a novel method for this problem. Firstly, considering the asynchronism of the input and output sampling signals, an auxiliary model is modeled as a medium to the state and output functions. Secondly, the Kalman prediction algorithm is improved to estimate the state values at output signals of the dual-rate system. The general step is using the output estimated errors in original and auxiliary systems to modify the estimated state values of the auxiliary model, and then the unknown state values in original system is defined by the ones in auxiliary model. Based on improved Kalman algorithm and hierarchical identification algorithm, we present the detailed procedures of state estimation and parameter identification method for the dual-rate system. The processes of state estimation and parameter identification are calculated and modified alternately. Finally, the simulation results reveal that the state and parameters both approach to the real values and the state values converge faster than the parameters.     

Searching bifurcations in high-dimensional parameter space via a feedback loop breaking approach

Bifurcations leading to complex dynamical behaviour of non-linear systems are often encountered when the characteristics of feedback circuits in the system are varied. In systems with many unknown or varying parameters, it is an interesting, but difficult problem to find parameter values for which specific bifurcations occur. In this article, we develop a loop breaking approach to evaluate the influence of parameter values on feedback circuit characteristics. This approach allows a theoretical classification of feedback circuit characteristics related to possible bifurcations in the system. Based on the theoretical results, a numerical algorithm for bifurcation search in a possibly high-dimensional parameter space is developed. The application of the proposed algorithm is illustrated by searching for a Hopf bifurcation in a model of the mitogen activated protein kinase cascade, which is a classical example for biochemical signal transduction.     

上下文分解机的自适应更新策略

分解机模型已经被成功应用于上下文推荐系统。在分解机模型的学习算法中,交替最小二乘法是一种固定其他参数只求单一参数最优值的学习算法,其参数数目影响计算复杂度。然而当特征数目很大时,参数数目随着特征数目急剧增加,导致计算复杂度很高;即使有些参数已经达到了最优值,每次迭代仍更新所有的参数。因此,主要改进了交替最小二乘法的参数更新策略,为参数引入自适应误差指标,通过权重和参数绝对误差共同决定该参数更新与否,使得每次迭代时重点更新最近两次迭代取值变化较大的参数。这种仅更新自适应误差大于阈值的参数的策略不但减少了需要更新的参数数目,进而加快了算法收敛的速度和缩短了运行时间,而且参数权重由误差决定,又修正了误差。在Yahoo和Movielens数据集上的实验结果证明:改进的参数更新策略运行效率有明显提高。