改进遗传算法的ERT有限元网格节点编号优化

在电阻层析成像中,有限元网格节点编号对整体刚度矩阵的带宽具有重要决定性的作用,而整体刚度矩阵的带宽直接决定了数据的存储量以及求解方程组的计算量。为了提高电阻层析成像中有限元的计算效率,减少数据存储量,利用改进遗传算法,对电阻层析成像中两种典型拓扑结构的有限元模型节点编号进行优化。实验结果表明,与常用节点编号规则相比,利用改进遗传算法可得到更优的带宽值,从而节省了计算机的内存空间,提高了计算效率。     

Remote tracking via encoded information for nonlinear systems

The problem addressed in this paper is to control a plant so as to have its output tracking (a family of) reference commands generated at a remote location and transmitted through a communication channel of finite capacity. The uncertainty due to the presence of the communication channel is counteracted by a suitable choice of the parameters of the regulator.     

基于DE算法的再入飞行器横向机动能力研究

再入飞行器横向机动能力分析可以描述为一个具有多约束的最优控制问题,不便于求解。本文将控制量攻角和倾侧角同时离散化,把约束最优控制问题转化为约束参数优化问题;推导攻角的取值范围,缩小搜索空间,降低问题的复杂性;用改进的约束DE算法求解该优化问题。以X-33为例对算法进行仿真验证,仿真结果表明所提算法能够很好的处理约束,且优化结果的鲁棒性较好。     

基于广义粗糙集的流程工业车间调度研究

针对流程工业某电化厂聚氯乙烯车间的生产过程,基于统一时间离散化方法,用广义粗糙集理论对投入产出比、设备转化率等不确定参数进行描述,建立了基于广义粗糙集有限中间存储的流程车间调度问题模型.该模型以最大化产值及精度要求为目标,考虑了设备容量约束、设备生产能力、物料平衡等多种约束,并应用基于改进进化策略的差分进化算法进行求解.仿真结果表明了该算法和模型的可行性及有效性.     

基于改进Bayesian-MCMC的突发水污染事件预测模型参数率定方法

预测模型是科学制定应急处置措施的基础.为快速准确地构建突发水污染事件预测模型,将预测模型参数的率定问题视为贝叶斯估计问题,并根据有限差分方法和贝叶斯推理得到参数的后验概率密度函数,再通过改进的Metropolis-Hastings抽样方法得到较为合理的参数值.以发生在某明渠段的突发水污染事件为例,分析讨论等容量控制非均匀流和非等容量控制非均匀流两种情景下不同观测噪声对参数率定值的影响,并与由贝叶斯-马尔科夫链蒙特卡罗方法得到的参数值和真实值进行对比.结果表明:改进Bayesian-MCMC方法在计算精度、适用性和抗噪声等方面优于贝叶斯-马尔科夫链蒙特卡罗方法,能较好地率定模型参数,并为构建突发水污染事件预测模型提供了新思路.     

Algorithms for determining capacities of individual buffers in assembly/disassembly systems

We deal with a buffer allocation problem of assembly/disassembly (AD) systems with a finite buffer capacity. The problem is to determine capacities of individual buffers for a given total available capacity of buffers with the objective of maximizing throughput rate. We first present an algorithm (BA-G) in which a conventional gradient search is used for finding an initial solution and a two-optimization procedure is used for improving the initial solution. Since this algorithm may require an excessive computation time, especially when a procedure for performance evaluation employed in the algorithm takes a long computation time, a new algorithm (BA-P) is developed in which a sophisticated procedure is used for finding an initial solution. In this procedure, capacities of individual buffers are determined based on the efficiency of their upstream and downstream machines. Results of computational experiments show that the latter algorithm gives the same solutions as those obtained by the former algorithm in much shorter time.     

Capacity and flexibility planning of an economical manufacturing system

This paper presents a simple dynamic model for determining the capacity and the flexibility of a manufacturing system over a finite planning horizon. We consider a problem that arises from the development phase of an investment plan for an economical manufacturing system. The objective is to minimize the total cost associated with the capacity expansion, flexibility expansion and operation. This problem is formulated as an integer program. A Lagrangian heuristic is developed for determining a near optimal solution to this integer program. Finally, we show how to incorporate aggregate production planning into the model.Based on a presentation given at the ORSA/TIMS Miami meeting in 1986.     

Augmented Lagrangian method applied to American option pricing

The American option pricing problem is originally formulated as a stochastic optimal stopping time problem. It is also equivalent to a variational inequality problem or a complementarity problem involving the Black-Scholes partial differential operator. In this paper, the corresponding variational inequality problem is discretized by using a fitted finite volume method. Based on the discretized form, an algorithm is developed by applying augmented Lagrangian method (ALM) to the valuation of the American option. Convergence properties of ALM are considered. By empirical numerical experiments, we conclude that ALM is more effective than penalty method and Lagrangian method, and comparable with the projected successive overrelaxation method (PSOR). Furthermore, numerical results show that ALM is more robust in terms of computation time under changes in market parameters: interest rate and volatility.     

多用户MIMO系统用户选择算法

MIMO系统在不增加带宽的情况下可以有效提高频谱效率。但由于多用户MIMO系统现存预编码方法和系统天线数目的限制,使得系统容纳用户数为一限定值,无法满足实际需要。针对这一问题,给出一种等功率分配的用户选择算法,其通过对信道容量等效近似来得到较优的用户组。为了降低用户选择复杂度,给出一种迭代的次优用户选择算法,达到快速选取较优用户的目的。仿真结果表明所提算法可以获得较好的系统容量性能,适用于实际通信系统。     

Prediction of uplift capacity of suction caissons using a neuro-genetic network

Suction caissons are frequently used for the anchorage of large offshore structures. The uplift capacity of the suction caissons is a critical issue that needs to be predicted reliably. A neuro-genetic model has been employed for this purpose. The neuro-genetic model uses the multilayer feed forward neural network (NN) as its host architecture and employs genetic algorithms to determine its weights. In comparison to the application of a conventional NN model [49] for the uplift capacity prediction problem, the application of a hybrid model such as the neuro-genetic network appears attractive. The conventional NN model is sensitive to training parameters and initial conditions and calls for a longer training of the network. Also it is not free of the inherent problem of settling for the local minimum in the neighborhood of the initial solution. In contrast, the hybrid model is much less sensitive to training parameters and initial conditions and inherently looks for a global optimum in a complex search space, which may be multimodal or non-differentiable, with a modest amount of training. The performance of the neuro-genetic model has been studied in detail over specific data sets pertaining to suction caissons, gathered from 12 independent studies [49] and compared with the predictions made by NN and finite element method models.     

MISO 系统基于正交匹配追踪算法的参数与时滞联合估计

在有限采样情况下, 研究具有时滞的多输入单输出受控自回归系统的参数辨识和时滞估计问题. 当采样次数少于未知变量数时, 描述系统的方程组是欠定的, 对其目标函数求解是NP-hard 问题, 传统方法无法有效辨识出系统参数. 受压缩感知理论的启发, 基于参数向量所具有的稀疏特性, 提出一种新的阈值正交匹配追踪算法辨识系统的参数和时滞. 仿真实验表明, 所提出的算法能在少量采样时有效地辨识系统参数、估计未知时滞, 同时验证了算法的有效性.

     

DS/CDMA系统中多址干扰的消除

在直接序列扩频/码分多址(DS/CDMA)系统中由于各用户之间的影响而使其容量受到很大限制。通常这种用户之间的影响是作为噪声来处理的。本文讨论了多址干扰问题,从另一不同的角度出发,即把用户之间的影响作为有用信息,对多个用户的数据信息进行联合判决,提出了提高判决准确性的方案。     

Reducing the computation required to solve a standard minimax problem

Minimax optimization problems have a long and rich history in the area of control. We show how the computation required to find the solution of a popular and widely applicable minimax problem can be significantly reduced. This reduction in computation results from an observation concerning the inner level (finite) maximization. In particular, we show that the number of parameter combinations that must be considered may be significantly reduced. We next indicate how this optimization problem can be used to synthesize a robust state feedback control for a system with parameter uncertainty. We conclude with an example robust control design problem that has 15 independent uncertain parameters and would not be practical were it not for the reduced computational requirement.     

On an output‐feedback stabilization problem with uncertainty in the relative degree

This paper deals with an output‐feedback finite‐time control problem for a class of nonlinear uncertain systems whose relative degree is affected by an uncertain system parameter and is therefore unknown at the stage of control design. We show that an existing second‐order sliding mode control algorithm can address successfully the control task of a finite‐time output‐feedback stabilization when the uncertain relative degree is equal to 1 or 2. We derive constructive tuning rules for the control parameters and show its effectiveness by using computer simulations. Copyright © 2007 John Wiley & Sons, Ltd.     

Numerical limit analysis of reinforced concrete slabs using a dual approach and conic programming

This study presents a numerical procedure for the analysis of reinforced concrete slabs (RCS) that obey Nielsen’s yield criterion. A pseudo lower bound formulation of finite elements is established to solve the static theorem as a conic optimization problem with the aim to determine the maximum load capacity of RCS. Lower bound solution is improved by means of an adaptive remeshing strategy using a dissipation estimator which is obtained on the normalization of the kinematic criterion defined from the yield criteria. Different known examples are evaluated and the results show a good agreement with the exact values of the collapse load as well as numerical convergence. The displacement rates are estimated from the dual solution established in the optimization problem in order to determine the collapse mechanism in the slab.     

Optimal control of unknown parameter systems

The problem is discussed of finding a cost functional for which an adaptive control law is optimal. The system under consideration is a partially observed linear stochastic system with unknown parameters. It is well known that an optimal finite-dimensional filter for this problem can be derived when the parameters belong to a finite set. Since the optimal filter involves the evaluation of a finite set of a posteriori probabilities for each of the parameter values given the observations, a natural adaptive control scheme is: (i) develop the optimal linear feedback law given each parameter; (ii) use the a posteriori probabilities to form the weighted average (convex combination) of the individual control policies; and (iii) use the weighted average as the control law. A quadratic cost functional is devised for which this strategy is optimal, in a general case, and it is shown that the probing effect identified with dual control problems is inherent in the standard linear-quadratic-Gaussian problem with parameter uncertainty     

Robust stability of polynomials with multilinear parameter dependence

The problem is studied of testing for stability a class of real polynomials in which the coefficients depend on a number of variable parameters in a multilinear way. We show that the testing for real unstable roots can be achieved by examining the stability of a finite number of corner polynomials (obtained by setting parameters at their extreme values), while checking for unstable complex roots normally involves examining the real solutions of up to m + 1 simultaneous polynomial equations, where m is the number of parameters. When m = 2, this is an especially simple task.     

Integration of generalized B-spline functions on Catmull–Clark surfaces at singularities

Subdivision surfaces are a common tool in geometric modelling, especially in computer graphics and computer animation. Nowadays, this concept has become established in engineering too. The focus here is on quadrilateral control grids and generalized B-spline surfaces of Catmull–Clark subdivision type. In the classical theory, a subdivision surface is defined as the limit of the repetitive application of subdivision rules to the control grid. Based on Stam’s idea, the labour-intensive process can be avoided by using a natural parameterization of the limit surface. However, the simplification is not free of defects. At singularities, the smoothness of the classically defined limit surface has been lost. This paper describes how to rescue the parameterization by using a subdivision basis function that is consistent with the classical definition, but is expensive to compute. Based on this, we introduce a characteristic subdivision finite element and use it to discretize integrals on subdivision surfaces. We show that in the integral representation the complicated parameterization reduces to a decisive factor. We compare the natural and the characteristic subdivision finite element approach solving PDEs on surfaces. As model problem we consider the mean curvature flow, whereby the computation is done on the step-by-step changing geometry.     

Scenario-based verification of uncertain parametric MDPs

We consider parametric Markov decision processes (pMDPs) that are augmented with unknown probability distributions over parameter values. The problem is to compute the probability to satisfy a temporal logic specification with any concrete MDP that corresponds to a sample from these distributions. As solving this problem precisely is infeasible, we resort to sampling techniques that exploit the so-called scenario approach. Based on a finite number of samples of the parameters, the proposed method yields high-confidence bounds on the probability of satisfying the specification. The number of samples required to obtain a high confidence on these bounds is independent of the number of states and the number of random parameters. Experiments on a large set of benchmarks show that several thousand samples suffice to obtain tight and high-confidence lower and upper bounds on the satisfaction probability.

     

A study of one class of NLP problems arising in parametric semi-infinite programming

The paper deals with a nonlinear programming (NLP) problem that depends on a finite number of integers (parameters). This problem has a special form, and arises as an auxiliary problem in study of solutions' properties of parametric semi-infinite programming (SIP) problems with finitely representable compact index sets. Therefore, it is important to provide a deep study of this NLP problem and its properties w.r.t. the values of the parameters. We are especially interested in the case when optimal solutions of the NLP problem satisfy certain properties due to some specific requirements arising in parametric SIP. We establish the values of the parameters for which optimal solutions of the corresponding NLP problem fulfil the needed properties, and suggest an algorithm that determines the right values of the parameters. An example is proposed to illustrate the application of the algorithm.