自适应混合多目标分布估计进化算法

针对多目标分布估计算法全局收敛性较弱的缺陷,提出了一种自适应混合多目标分布估计进化算法。其基本思想是:在多目标分布估计算法中引入全局收敛性较强的差分进化算法,当函数变化率较大时,用分布估计算法产生新种群;当函数变化率较小即算法可能陷入局部收敛时,用差分进化算法产生新种群。理论分析和数值实验结果表明,这种混合算法不仅具有良好的全局收敛性,而且解的分布性和均匀性较没有考虑目标函数变化率的混合多目标分布估计算法也有了一定程度的提高。     

无理数字典码的测试数据压缩方案

提出了一种无理数字典码的测试数据压缩方案,能有效地压缩芯片测试数据。方案利用无理数序列建立字典,编码时只需进行相容性检查,无需填充无关位,简化了操作步骤;同时,选择局部压缩率最大的一组数据作为最终编码结果,保证压缩效果可以达到局部最优。对ISCAS 89标准电路Mintest集的实验结果显示,该方案的压缩效果优于传统压缩方法,如Golomb码、FDR码、混合定变长码等。     

多传感器数据采集系统中的数据融合研究

针对多传感器数据采集系统的准确性与可靠性的要求,提出了单传感器分批估计融合与模糊理论中的相关性函数与加权自适应算法相结合的数据融合方法。首先,对每一只传感器进行分批估计融合,得到了较为可靠的局部决策值。然后,利用模糊理论中的相关性函数,计算出每只传感器与其它传感器的相关程度,并剔除掉那些相关程度较低的传感器的局部决策值。最后,在权的最优分配原则下,利用加权自适应算法对剩下的局部决策值进行数据融合。该方法能在数据先验知识未知的情况下,客观地反映各个传感器的可靠程度。数据分析结果表明:与同类融合方法相比,该方法具有更高的精度。     

长河坝水电站初期导流洞下闸封堵方案优选研究

长河坝水电站初期导流洞堵头挡正常蓄水位的水头高达212.00 m,封堵工程量大、程序复杂、直接影响电站发电工期。结合导流洞封堵施工的特点,通过结构稳定计算分析,拟定永久堵头挡水、闸门挡水、临时堵头与永久堵头结合、2号导流洞堵头位置下移4种下闸封堵方案,并从发电工期、施工进度、工程结构安全、工程投资等方面进行全面的经济技术比较,进而优选出最佳方案。研究成果可为类似高堆石坝工程导流洞的下闸封堵设计提供借鉴。     

Optimal linear filter design for systems with correlation in the measurement matrices and noises: recursive algorithm and applications

This paper addresses the optimal least-squares linear estimation problem for a class of discrete-time stochastic systems with random parameter matrices and correlated additive noises. The system presents the following main features: (1) one-step correlated and cross-correlated random parameter matrices in the observation equation are assumed; (2) the process and measurement noises are one-step autocorrelated and two-step cross-correlated. Using an innovation approach and these correlation assumptions, a recursive algorithm with a simple computational procedure is derived for the optimal linear filter. As a significant application of the proposed results, the optimal recursive filtering problem in multi-sensor systems with missing measurements and random delays can be addressed. Numerical simulation examples are used to demonstrate the feasibility of the proposed filtering algorithm, which is also compared with other filters that have been proposed.     

Finite horizon optimal control of non-linear discrete-time switched systems using adaptive dynamic programming with ε-error bound

In this paper, we aim to solve the finite-horizon optimal control problem for a class of non-linear discrete-time switched systems using adaptive dynamic programming(ADP) algorithm. A new ε-optimal control scheme based on the iterative ADP algorithm is presented which makes the value function converge iteratively to the greatest lower bound of all value function indices within an error according to ε within finite time. Two neural networks are used as parametric structures to implement the iterative ADP algorithm with ε-error bound, which aim at approximating the value function and the control policy, respectively. And then, the optimal control policy is obtained. Finally, a simulation example is included to illustrate the applicability of the proposed method.     

Coordinating efficiency and equity in disaster relief logistics via information updates

This paper addresses a multi-supplier, multi-affected area, multi-relief, and multi-vehicle relief allocation problem in disaster relief logistics. A multi-objective optimisation model based on disaster scenario information updates is proposed in an attempt to coordinate efficiency and equity through timely and appropriate decisions regarding issues such as vehicle routing and relief allocation. An optimal stopping rule is also proposed to determine the optimum period of delay before responding to disaster, because decision making requires accurate disaster information. The main contribution of this paper is solving relief allocation problem in a novel way by correlating operational research with statistical decision making and Bayesian sequential analysis. Finally, a case is presented based on the post-disaster rescue in Eastern China after supertyphoon Saomai to test the applicability and show the potential advantages of the proposed model.     

On constrained infinite-time nonlinear optimal control

In this work, we consider the infinite-time optimal control of input affine nonlinear systems subject to point-wise in time inequality constraints on both the process inputs and outputs. Fundamental to solving this constrained infinite-time nonlinear optimal control (CITNOC) problem is the ability to calculate the value function of it's unconstrained counterpart, the infinite-time nonlinear optimal control (ITNOC) problem. Unfortunately, the traditional ITNOC solution procedure of specifying an objective function and then solving for the optimal control policy and corresponding value function is computationally intractable in all but the simplest of examples. However, in many cases one can easily identify a stabilizing feedback for near operating point regulation. Building from this local policy, the proposed method is to construct a meaningful optimal control objective function as well as its corresponding value function. These functions are then used to analyze the closed-loop stability of the proposed policy. Upon return to the constrained case the constructed objective and value functions are again used to develop a self-consistent constrained finite-time scheme that will, for the first time, provide an exact solution to the CITNOC problem. The mechanics of the proposed method are then illustrated by an example from chemical reactor control.     

Explicit solution of relative entropy weighted control

We consider the minimization over probability measures of the expected value of a random variable, regularized by relative entropy with respect to a given probability distribution. In the general setting we provide a complete characterization of the situations in which a finite optimal value exists and the situations in which a minimizing probability distribution exists. Specializing to the case where the underlying probability distribution is Wiener measure, we characterize finite relative entropy changes of measure in terms of square integrability of the corresponding change of drift. For the optimal change of measure for the relative entropy weighted optimization, an expression involving the Malliavin derivative of the cost random variable is derived. The theory is illustrated by its application to several examples, including the case where the cost variable is the maximum of a standard Brownian motion over a finite time horizon. For this example we obtain an exact optimal drift, as well as an approximation of the optimal drift through a Monte-Carlo algorithm.     

ZW多产品间歇过程调度及在线调整

针对多产品间歇过程调度提出了分层递阶的Petri网建模方法，利用赋时Petri和Petri网的简化技术描述不同层次的生产问题，具有很强的模型描述能力。另外，在调度决策层可以方便地集成优化策略和在线调整算法，使问题求解更加灵活。