多渠道分销库存系统成本模型

考虑由一个制造商、多个分销中心、多个零售商组成的多级分销系统。针对多渠道分销问题,在周期检查库存策略的前提下,分别建立分销中心持有库存和分销中心不持有库存两种情况下,零售商具有随机需求及相关需求的多级库存系统成本优化模型。最后,通过算例,从系统总成本最小的角度出发,给出了这两种条件下库存成本随需求相关系数的变化情况,证明了此模型可用于库存决策。     

Stability analysis of the simplest Takagi-Sugeno fuzzy control system using circle criterion

A frequency-domain-based sufficient condition is derived to guarantee the globally asymptotic stability of the simplest Takagi-Sugeno (T-S) fuzzy control system by using the circle criterion. The analysis is performed in the frequency domain, and hence the condition is of great significance when the frequency-response method, which is widely used in the linear control theory and practice, is employed to synthesize the simplest T-S fuzzy controller. Besides, this sufficient condition is featured by a graphical interpretation, which makes the condition straightforward to be used. Comparisons are drawn between the performance of the simplest T-S fuzzy controller and that of the linear compensator. Two numerical examples are presented to demonstrate how this sufficient condition can be applied to both stable and unstable plants.     

A multiple intelligent agent system for credit risk prediction via an optimization of localized generalization error with diversity

Company bankruptcies cost billions of dollars in losses to banks each year. Thus credit risk prediction is a critical part of a bank＇s loan approval decision process. Traditional financial models for credit risk prediction are no longer adequate for describing today＇s complex relationship between the financial health and potential bankruptcy of a company. In this work, a multiple classifier system （embedded in a multiple intelligent agent system） is proposed to predict the financial health of a company. In our model, each individual agent （classifier） makes a prediction on the likelihood of credit risk based on only partial information of the company. Each of the agents is an expert, but has limited knowledge （represented by features） about the company. The decisions of all agents are combined together to form a final credit risk prediction. Experiments show that our model out-performs other existing methods using the benchmarking Compustat American Corporations dataset.     

From Researching Regions at a Distance to Participatory Network Building: Integrating Action Research and Economic Geography

This paper describes the change in a researcher's practices from one of studying regional industrial development at a distance to one of working closely with a network of agri-food managers. This is accompanied by a discussion of the methodological and theoretical possibilities for an action research inspired economic geography. Some of the core characteristics of a pragmatic conceptualization of action research are revised. It is argued that theoretical developments within economic geography make possible a dialogue between researchers and local actors and that economic geography is challenged to accept that knowledge is generated through such a dialogue. Attempts to create a dialogue between action research and economic geography in a specific Norwegian industrial and geographical context are described. These attempts indicate that the researcher's methodological approach has been transformed from a narrow one relying solely on conventional social science methods to a broader one including participatory action research.     

ROLE OF α-PSEUDO-UNIVEX FUNCTIONS IN VECTOR VARIATIONAL-LIKE INEQUALITY PROBLEMS

In this paper, we introduce a new class of generalized convex function, namely, a-pseudounivex function, by combining the concepts of pseudo-univex and α-invex functions. Further, we establish some relationships between vector variational-like inequality problems and vector optimization problems under the assumptions of α-pseudo-univex functions. Results obtained in this paper present a refinement and improvement of previously known results.     

用神经网络实现线性系统状态估计

本文研究了一种用神经网络解决线性动态系统状态估计问题的方法。使用了动态优化技术设置Ｈｏｐｆｉｅｌｄ网络的加权矩阵。仿真结果显示本文研究的方法具有广泛的适用性。此方法对于大型系统实时估计具有很大的应用潜力。     

Multi-label dimensionality reduction and classification with extreme learning machines

In the need of some real applications, such as text categorization and image classification, the multi-label learning gradually becomes a hot research point in recent years. Much attention has been paid to the research of multi-label classification algorithms. Considering the fact that the high dimensionality of the multi-label datasets may cause the curse of dimensionality and wil hamper the classification process, a dimensionality reduction algorithm, named multi-label kernel discriminant analysis （MLKDA）, is proposed to reduce the dimensionality of multi-label datasets. MLKDA, with the kernel trick, processes the multi-label integrally and realizes the nonlinear dimensionality reduction with the idea similar with linear discriminant analysis （LDA）. In the classification process of multi-label data, the extreme learning machine （ELM） is an efficient algorithm in the premise of good accuracy. MLKDA, combined with ELM, shows a good performance in multi-label learning experiments with several datasets. The experiments on both static data and data stream show that MLKDA outperforms multi-label dimensionality reduction via dependence maximization （MDDM） and multi-label linear discriminant analysis （MLDA） in cases of balanced datasets and stronger correlation between tags, and ELM is also a good choice for multi-label classification.     

基于储备容量的交通网络潜在瓶颈路段识别

交通网络潜在瓶颈路段是随着需求变化将来可能会成为瓶颈的路段.准确识别出潜在瓶颈路段并进行优化管理,可以更好地适应未来交通需求的变化.基于交通网络储备容量模型,提出了交通网络潜在瓶颈路段识别方法,并设计了储备容量模型的求解算法.算例分析比较了现状需求和路网可容纳最大需求下的交通均衡流量模式.结果表明,不能简单地根据现状需求去推断潜在瓶颈路段,而论文提出的方法可以识别出路网达到最大需求时的潜在瓶颈路段.此外,利用交通网络储备容量模型设计了一种次要潜在瓶颈路段识别方法,次要潜在瓶颈路段是在优化潜在瓶颈路段之后随需求增长新出现的瓶颈路段.算例表明该识别方法具有一定的适用性.     

基于小世界网络的微博谣言传播演进研究

刻画谣言在微博上的快速传播状态,研究影响谣言传播的关键因素。基于传染病的基础模型,将受众扩展为5类(无知者、接触者、传播者、沉寂者、失去兴趣者),引入兴趣衰减系数描述个体多次接触谣言时转发兴趣降低状态,同时考虑了个体只会转发一次的现实状况。为验证模型的有效性,对模型进行了多主体仿真,并将仿真数据与两个真实案例的数据对比,发现构建的模型可以较好地拟合现实情况。通过仿真实验,对不同因素的系数进行对比分析,发现改变兴趣衰减系数、首次转发概率以及小世界网络的属性都显著影响了微博的传播演进过程。     

MIMO-OFDM系统中联合时域和空间域信号处理的改进PTS算法

为了降低多输入多输出正交频分复用(multiple input multiple output orthogonal frequency division multiplexing, MIMO-OFDM)系统中传统部分传输序列(partial transmit sequence, PTS)算法的计算复杂度,提出了联合时域和空间域信号处理的改进PTS算法。在时域信号处理部分,通过信号子块循环移位实现备选序列的增加;在空间域部分,利用天线间信号子块交换实现峰均功率比(peak to average power ratio, PAPR)抑制。同时在接收端 ,利用子块相位旋转引起的相位差异,本方法通过比较接收信号与星座点的距离,可以实现信号的盲检测,从而有效高MIMO-OFDM系统的频谱利用率。仿真结果表明,提出的方法能有效地抑制MIMO-OFDM信号的PAPR,而且明显降低了传统PTS算法的计算复杂度,同时可获得跟传统PTS方法已知边带副信息时相似的比特误码率(bit error rate, BER)性能。