混合FLN－Lagrange松驰法用于机组最优投入

本文提出用混合函数链网络与Ｌａｇｒａｎｇｅ松驰法解机组最优投入问题．基于神经网络的监督学习和自适应模式识别概念，ＦＬＮ被用来预测负荷需求与Ｌａｇｒａｎｇｅ乘子之间的关系．为了证实这一方法的有效性，一个具有１６台电机组的实际系统被测试．数值计算结果表明系统发电总成本可获得最少，大大减少了计算时间．     

Job Shop 调度的序列拉格朗日松驰法

拉格朗日松驰法为求解复杂调度问题次最优解的一种重要方法，陆宝森等人把这种方法推广到Ｊｏｂ Ｓｈｏｐ调度问题，但他们的方法存在解振荡问题。本文提出一种序列拉格朗日松驰法，它能避免解振荡。     

实时无等待HFS调度的一种拉格朗日松弛算法

研究了实时无等待HFS调度问题,并建立一个整数规划模型,提出运用拉格朗日松弛算法来求解,在此算法中,常采用次梯度方法更新拉格朗日乘子,但它随着迭代数的增加收敛速度会减慢,因此设计了一个改进的bundle方法。将以前的次梯度累积到bundle中,以获得一个更好的乘子更新方向．仿真实验表明,与次梯度方法相比,所设计的bundle法不仅在较少的迭代数内得到了更快的收敛速度而且改进了优化性能,对于大规模问题效果更为显著。     

一种非凸松弛化的多核集成回归方法

支持向量机的核函数的应用性越来越强.性能优秀的核函数可以带来非常好的效果,为了充分利用核函数的优点,构造出一种创新的多核集成方法.在这个整体框架中,每个核回归器都与一个权重相关联,该权重可以根据其对回归结果的贡献来自训练自动调整.通过这种方式,可以直接从数据中学习更合适的核函数类别及其对应的参数,而无需任何人工干预,从...     

一种新的机组分配优化方法

考虑到电力系统负荷特性以及专家经验,提出了Lagrangian分解协调与专家规则相结合求解机组分配的优化方法,该方法以专家规则替代动态规划部分,因而使算法得到简化,大大减少了计算量。实例表明该方法正确可行,在电力系统短期优化调度中具有现实意义。     

A Hybrid Bayesian-Frequentist Approach to SLAM

A hybrid Bayesian/ frequentist approach is presented for the Simultaneous Localization and Mapping Problem (SLAM). A frequentist approach is proposed for mapping a dense environment when the robotic pose is known and then extended to the case when the pose is uncertain. The SLAM problem is then solved in two steps: 1) the robot is localized with respect to a sparse set of landmarks in the map using a Bayes filter and a belief on the robot pose is formed, and 2) this belief on the robot pose is used to map the rest of the map using the frequentist estimator. The frequentist part of the hybrid methodology is shown to have complexity linear (constant time complexity under the assumption of bounded noise) in the map components, is robust to the data association problem and is provably consistent. The complexity of the Bayesian part is kept under control owing to the sparseness of the features, which also improves the robustness of the technique to the issue of data association. The hybrid method is tested on standard datasets on the RADISH repository.     

A New Approach to Unit Root Testing

A novel simulation based approach to unit root testing is proposed in this paper. The test is constructed from the distinct orders in probability of the OLS parameter estimates obtained from a spurious and an unbalanced regression, respectively. While the parameter estimate from a regression of two integrated and uncorrelated time series is of order O _p (1), the estimate is of order O _p (T ⁻¹) if the dependent variable is stationary. The test statistic is constructed as an interquantile range from the empirical distribution obtained from regressing the standardized data sufficiently often on controlled random walks. GLS detrending (Elliott et al., Econometrica 64(4):813–836, 1996) and spectral density variance estimators (Perron and Ng, Econom Theory 14(5):560–603, 1998) are applied to account for deterministic terms and residual autocorrelation in the data. A Monte Carlo study confirms that the proposed test has favorable empirical size properties and is powerful in local-to-unity neighborhoods. As an empirical illustration, we test the purchasing power parity hypothesis for a sample of G7 economies.     

逐步次梯度法在基于LR的调度算法中的应用

在基于拉格朗日松弛法（LR）的优化调度算法中,对偶问题的求解广泛采用的一种方法是次梯度法：在这个方法中,为了得到一个次梯度方向,相应松弛问题的所有的子问题都必须精确求解,当问题规模较大时求解时间过长。讨论了逐步次梯度法求解对偶问题的具体实现方法。将对偶函数化为多个子项和的形式,每求解一个子问题,就构造对应对偶函数一个子项的次梯度,逐步沿这些次梯度方向更新乘子。仿真结果显示,其收敛速度较原始的次梯度法有明显的提高：     

A Hybrid Approach to Nonconformance Tracking and Recovery

The nonconformance diagnosis problem has been a major issue facing industry and academia over the years. Research has been carried out on technologies for different aspects of nonconformance diagnosis such as nonconformance monitoring, prediction, prevention, classification, tracking, and recovery. Despite these advances, nonconformance tracking and recovery still receive many concerns due to the fact that they are knowledge intensive and experience-based tasks, which in complex manufacturing environments can sometimes be beyond the capabilities of skilled operators and engineers. In addition, the existing systems for nonconformance tracking and recovery are usually special purpose systems. They lack the capabilities to migrate to new working domains. This paper proposes a generic intelligent nonconformance tracking and recovery (GINTR) system. In conjunction with computational intelligent techniques such as Artificial Neural Networks (ANN) and Genetic Algorithm (GA), the system identifies the root causes of a nonconformance and provides timely corrective actions. The drive towards designing such a system is motivated by the need to implement a generic base of system capabilities that is reliable, economical, scalable, and provides a stable foundation for migrating the system to different domains.     

A Lagrangian Approach for Multiple Personalized Campaigns

The multicampaign assignment problem is a campaign model to overcome the multiple-recommendation problem that occurs when conducting several personalized campaigns simultaneously. In this paper, we propose a Lagrangian method for the problem. The original problem space is transformed to another simpler one by introducing Lagrange multipliers, which relax the constraints of the multicampaign assignment problem. When the Lagrangian vector is supplied, we can compute the optimal solution under this new environment in O(NK2) time, where N and K are the numbers of customers and campaigns, respectively. This is a linear-time method when the number of campaigns is constant. However, it is not easy to find a Lagrangian vector in exact accord with the given problem constraints. We thus combine the Lagrangian method with a genetic algorithm to find good near-feasible solutions. We verify the effectiveness of our evolutionary Lagrangian approach in both theoretical and experimental viewpoints. The suggested Lagrangian approach is practically attractive for large-scale real-world problems.