A Priority-Based Negotiations Approach for Handling Inconsistencies in Multi-Perspective Software Requirements

Inconsistency of multi-perspective requirements specifications is a pervasive issue during the requirements process. However, managing inconsistency is not just a pure technical problem. It is always associated with a process of interactions and competitions among corresponding stakeholders. The main contribution of this paper is to present a negotiations approach to handling inconsistencies in multi-perspective software requirements. In particular, the priority of requirements relative to each perspective plays an important role in proceeding negotiation over resolving inconsistencies among different stakeholders. An algorithm of generating negotiation proposals and an approach to evaluating proposals are also presented in this paper, respectively.     

一个证券组合投资分析的对策论方法

将给出证券组合投资分析的一个对策论方法 .将最小的可能收益作为风险的度量 ,将投资者的投资心理及证券市场的随机性考虑到模型之中 ,建立了证券组合投资的双层规划模型 ;对模型进行了理论分析、提出了求解算法 ;通过一个例子来说明如何用本文方法来进行证券组合投资分析.     

A KELM-Based Ensemble Learning Approach for Exchange Rate Forecasting

In this paper, a KELM-based ensemble learning approach, integrating Granger causality test, grey relational analysis and KELM(Kernel Extreme Learning Machine), is proposed for the exchange rate forecasting. The study uses a set of sixteen macroeconomic variables including, import,export, foreign exchange reserves, etc. Furthermore, the selected variables are ranked and then three of them, which have the highest degrees of relevance with the exchange rate, are filtered out by Granger causality test and the grey relational analysis, to represent the domestic situation. Then, based on the domestic situation, KELM is utilized for medium-term RMB/USD forecasting. The empirical results show that the proposed KELM-based ensemble learning approach outperforms all other benchmark models in different forecasting horizons, which implies that the KELM-based ensemble learning approach is a powerful learning approach for exchange rates forecasting.     

探讨动态复杂网络的新途径

指出了BA模型分析过程的缺陷,考虑了复杂网络节点的到达时间间隔分布和寿命分布,将复杂网络与排队系统联系起来,从方法论上取得了突破,为复杂网络的研究提供了新途径.利用Markov链理论分析了G-M-P网络,获得了这类网络稳态度分布计算公式.     

An Approach to RCS Computation for Complex Targets

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优化差异工件单机批调度问题的混合微粒群算法

研究了单机环境下具有动态到达时间的差异工件批调度问题,设计了微粒群算法对此类问题进行求解,并结合动态规划进行优化.首先给出了问题的微粒表达形式,并根据问题的离散优化特性对微粒状态的更新方法进行了改进;然后将微粒群算法和动态规划算法进行有效结合,改善近似解的质量.在实验中,对各类不同规模的算例均进行了仿真,验证了该算法的有效性.     

证券组合投资分析的进化博弈方法

给出证券组合选择的一个进化博弈方法。将证券市场看成投资者群体博弈，其中财富流入更成功的投资策略，证券的价格由市场出清条件决定。在投资者为风险中性的假设下，我们指出惟一的纯策略Nash均衡是所有投资者按证券的(归一化后)期望收益成比例地分配财富，即根据证券的基本价值投资。进一步我们证明了该Nash均衡策略是Schaffer意义下的进化稳定策略。     

区域导航进近阶段导航台选择模型与仿真

导航台自动选择是区域导航重要组成部分,根据进近阶段VOR／DME和DME／DME区域导航要求设计了VOR／DME无线电导航台自动选择算法,引入了DME纽对有效导航距离,同时加入了合理的导航台切换准则。利用在MALAB Simulink下建立的VOR／DME导航台选择及区域导航仿真模型,采用广州白云机场以及北京首都机场的区域导航进近航路进行了仿真实验。实验表明该算法能选择有效的导航台,在及时切换为更好的导航台同时能避免导航台切换过于频繁。     

Predicting Trend of High Frequency CSI 300 Index Using Adaptive Input Selection and Machine Learning Techniques

High-frequency stock trend prediction using machine learners has raised substantial interest in literature. Nevertheless, there is no gold standard to select the inputs for the learners. This paper investigates the approach of adaptive input selection(AIS) for the trend prediction of high-frequency stock index price and compares it with the commonly used deterministic input setting(DIS) approach.The DIS approach is implemented through computation of technical indicator values on deterministic period parameters. The AIS approach selects the most suitable indicators and their parameters for the time-varying dataset using feature selection methods. Two state-of-the-art machine learners, support vector machine(SVM) and artificial neural network(ANN), are adopted as learning models. Accuracy and F-measure of SVM and ANN models with both the approaches are computed based on the high-frequency data of CSI 300 index. The results suggest that the AIS approach using t-statistics,information gain and ROC methods can achieve better prediction performance than the DIS approach.Also, the investment performance evaluation shows that the AIS approach with the same three feature selection methods provides significantly higher returns than the DIS approach.     

一种基于模型共享的半监督多标签图像学习法

提出一种快速且有效的半监督多标签学习方法:模型共享半监督推举。该方法能发现、共享并组合多个基模型,每个基模型是在某个标签上利用半监督支持向量机(S3VM)上学习的。通过使用模型共享,标签关联被显示地利用且对于每个标签来说只需要少量的基模型即可生成最后的决策结果。在Corel5k和Mediamill数据集上评估方法,实验结果显示的方法与当前流行的监督和半监督多标签学习方法是可比的。     

面向复杂仿真因果追溯的行为建模方法

基于行为建模的因果追溯方法是解决复杂仿真分析中因果关系追溯问题的一种思想。该思想依据仿真系统运行结果数据,重新构建描述因果关系的行为模型,并基于这些行为模型进行因果追溯。为了建立良好有效的行为模型支持追溯,首先介绍了因果关系的概念及其结构描述;然后将追溯划分为三个层次:系统表层、系统深层以及组合层,最后选取事件图行为模型为基本模型,按照这些层次分别对其进行扩展,从而获得不同层次的行为建模方法支持因果关系的描述和建模。     

Learning to Predict in Complex Biological Domains

1 Introduction1 Bioinformatics is a rapidly expanding field involving a significant contribution from the data mining community. Many different problems are grouped into the general category of bioinformatics, including protein secondary structure prediction, drug discovery, DNA microarray analysis, gene prediction and genome analysis [17]. In this paper, we focus on two particular problems in which data mining can contribute: drug discovery and protein secondary structure prediction. Both …     

柔性机器加工排序问题中的遗传算法

现代制造系统中,计算机辅助加工计划(CAPP)起着非常重要的作用,它是从计算机辅助设计(CAD)到计算机辅助制造(CAM)不可缺少的中间环节。在允许作业柔性加工的基础上,本文提出一种新的两目标机器加工排序问题。由于该加工排序是一个NP-完全问题,进而构造了一个新的遗传算法求解该问题的非劣解解集。数值计算表明,该方法对两目标的柔性加工排序问题是有效的。     

广义支持向量机优化问题的极大熵方法

依据最优化理论中的KKT互补条件建立了广义支持向量机的无约束优化模型,并给出了一种有效的光滑化近似解法极大熵方法,为求解支持向量机优化问题提供了一种新途径.该方法特别易于计算机实现,理论分析和数值实验结果表明了模型和算法的可行性和有效性.     

基于机器学习参数选择的交通图像编码方法

将图像压缩与ITS交通路况图像电传视讯相结合,提出了一种基于机器学习参数选择的多项式拟合图像压缩编码方法.我们定义了两类指标来度量数据扫描方法对多项式拟合方法的影响;还研究了用机器学习方法选择得到的参数对图像扫描数据进行单调化处理;进而研究了用多项式拟合预处理数据的方法进行图像数据压缩.该法简单方便、快速高效,并已针对小幅复杂交通路况图像在中低信噪比取得了良好的结果.该法还可以推广到感兴趣区位于图像中心的一类图像.     

基于信任过滤的网格资源选择方法设计与仿真

该方法首先根据用户信任需求过滤低可信资源,然后对剩余高可信资源综合考虑其价格和风险因素,最后给出了最小机会成本启发式算法.实验表明,该方法能对供求双方产生激励:对资源方保证可靠资源提供者的整体利润;对用户方能显著降低作业失效率、减少成本.     

一种复杂曲柄摇杆机构运动学的计算机仿真

插秧机构曲柄摇杆机构运动十分复杂，教学中依靠繁琐的公式推导，既枯燥又不直观，况且随着杆件及运动参数的变化，其运动形式千变万化。利用SIMULINK平台，首先建立了数学模型，进行了实时仿真，输出了运动轨迹，插秧速度，加速度等图形。同时将机械优化设计结果进行仿真并绘出曲线，结合曲柄摇杆机构静态，运动及田间工作实况拍成的录像片，应用于多媒体教学中，收到的良好的效果。     

基于极限学习机的生化过程软测量建模

针对极限学习机方法隐层神经元数目过多的缺陷，提出一种改进的极限学习机方法。在单隐层前向神经网络的隐层中，增加一类分类神经元，从而形成了一种新的单隐层神经网络结构。针对不同类样本数不相同的问题，提出了处理方法，使得可以利用相同的隐层神经元对不同类的学习样本进行拟合，这使得网络的隐层神经元数目大大降低，从而简化了模型的结构，提高了神经网络的计算速度。将这一方法应用于诺西肽发酵过程，建立了菌体浓度的软测量模型，实现了菌体浓度的在线预估。     

经济复杂性系统主体学习算法理论综述

对经济复杂性系统研究文献中常见的主体学习算法进行了一个全面的梳理.将学习算法分为来自智能计算或演化计算的学习算法以及来自心理学和实验经济学的学习算法,并结合文献介绍了各类学习模型在经济学研究中的主要应用.还回顾了文献中对各种算法的分析比较,并对学习算法的发展前景进行了展望.由于学习算法还是一个相对较新的领域,对经济系统中个体学习行为需要进行更加深入的研究和探索.     

A Two-Dimensional Approach to Iterative Learning Control with Randomly Varying Trial Lengths

In this paper, iterative learning control(ILC) is considered to solve the tracking problem of time-varying linear stochastic systems with randomly varying trial lengths. Using the two-dimensional Kalman filtering technique, the authors can establish a recursive framework for designing the learning gain matrix along both time and iteration axes by optimizing the trace of input error covariance matrix.It is strictly proved that the input error converges to zero asymptotically in mean square sense and thus the tracking error covariance converges. The extensions to that prior distribution of nonuniform trial lengths is unknown are also investigated with an asymptotical estimation method. Numerical simulations are provided to verify the effectiveness of the proposed framework.