基于云计算的营销决策支持系统

设计并实施了一种基于云计算的营销决策支持系统,和其他管理决策子系统发生交互,共同构成了完整的现代企业经营决策支持系统.系统的数据库采用分布式设计,使得系统既有独立处理本地数据库的能力,也可读取异地数据库中的数据.系统模型库包含多种营销因素的决策模型,并采用模型组合的思想,将复杂的决策问题通过模型之间的组合来实现.在流程设计上充分考虑人机交互,将用户的经验判断纳入到决策过程中.完成了系统的开发,并用仿真数据进行了系统测试,结果表明系统基本运行稳定,各子模块衔接良好,与其他子系统实现了数据共享.     

Decision support for Cybersecurity risk planning

Security countermeasures help ensure the confidentiality, availability, and integrity of information systems by preventing or mitigating asset losses from Cybersecurity attacks. Due to uncertainty, the financial impact of threats attacking assets is often difficult to measure quantitatively, and thus it is difficult to prescribe which countermeasures to employ. In this research, we describe a decision support system for calculating the uncertain risk faced by an organization under cyber attack as a function of uncertain threat rates, countermeasure costs, and impacts on its assets. The system uses a genetic algorithm to search for the best combination of countermeasures, allowing the user to determine the preferred tradeoff between the cost of the portfolio and resulting risk. Data collected from manufacturing firms provide an example of results under realistic input conditions.     

基于多媒体多智能Agents系统的群体决策支持系统集成化设计研究

分析了Ｄeｓａｎｅｔｉｓ＆Ｇａｌｌｕｐｅ群体决策支持系统（Ｇｒｏｕｐ Ｄｅｃｉｓｉｏｎ Ｓｕｐｐｏｒｔ,简称ＧＤＳＳ）初步设计的通用模式,提出了将多媒体技术、智能Ａｇeｎｔs技术、数据仓库技术Ｉｎｔｅｒｎｅｔ／Ｉｎｔｒａｎｅｔ技术等有机地集成于ＧＤＳＳ的集成化设计方案,并就其关键技术功能结构方案设计进行了研究．     

Identification and control of open-loop unstable processes by relay methods

This paper describes a reliable automatic PID tuning method for open-loop unstable processes. Identification with low order models is performed by means of two relay tests, one with an additional delay, which does not require a priori knowledge about the process, with the only necessary condition being that the process be gain stabilisable. This paper provides an overview of the method, states conditions that need to be satisfied for its successful implementation, and demonstrates its application on a number of examples.     

Least squares support vector machines with tuning based on chaotic differential evolution approach applied to the identification of a thermal process

In the past decade, support vector machines (SVMs) have gained the attention of many researchers. SVMs are non-parametric supervised learning schemes that rely on statistical learning theory which enables learning machines to generalize well to unseen data. SVMs refer to kernel-based methods that have been introduced as a robust approach to classification and regression problems, lately has handled nonlinear identification problems, the so called support vector regression. In SVMs designs for nonlinear identification, a nonlinear model is represented by an expansion in terms of nonlinear mappings of the model input. The nonlinear mappings define a feature space, which may have infinite dimension. In this context, a relevant identification approach is the least squares support vector machines (LS-SVMs). Compared to the other identification method, LS-SVMs possess prominent advantages: its generalization performance (i.e. error rates on test sets) either matches or is significantly better than that of the competing methods, and more importantly, the performance does not depend on the dimensionality of the input data. Consider a constrained optimization problem of quadratic programing with a regularized cost function, the training process of LS-SVM involves the selection of kernel parameters and the regularization parameter of the objective function. A good choice of these parameters is crucial for the performance of the estimator. In this paper, the LS-SVMs design proposed is the combination of LS-SVM and a new chaotic differential evolution optimization approach based on Ikeda map (CDEK). The CDEK is adopted in tuning of regularization parameter and the radial basis function bandwith. Simulations using LS-SVMs on NARX (Nonlinear AutoRegressive with eXogenous inputs) for the identification of a thermal process show the effectiveness and practicality of the proposed CDEK algorithm when compared with the classical DE approach.     

舰船综合保障中数据综合管理研究

介绍了舰船综合保障数据综合管理的有关内容，包括信息数据分类、数据综合管理功能分类以及综合保障数据库的设计思路．根据舰船的特点设计了舰船综合保障数据管理系统的硬件框架、软件体系、开发以度数据库工具．所研究的设计思路已在某船舶综合保障信息管理系统中得到应用,证明该设计模式是科学、可行的     

神经网络在煤矿安全决策支持系统中的研究及应用

主要研究人工神经网络在煤矿安全决策支持系统中的应用。将BP网络用于瓦斯突出,解决了瓦斯突出判别困难、难以建立数学模型的难题,为煤矿瓦斯突出研究找到了一种新的建模和预测方法。     

改进分块支持向量聚类在入侵检测中的应用

支持向量聚类(SVC)是在支持向量机的思想上发展而来一种聚类方法,针对其处理大规模数据集速度缓慢的缺点,提出了一种改进的分块支持向量聚类算法。改进的算法分为三个阶段:前期的预处理,中期的分块算法,后期的改进标类算法。提出的方法显著加快了SVC的速度,在保持原来SVC算法的优点的基础上,对大规模数据集以及非均匀分布数据集等具有良好的效果。将其应用到网络入侵检测,实验结果表明改进的算法行之有效。     

面向业务分析与预测的企业决策支持系统设计

为了提高企业决策自动化管理水平，运用先进的数据仓库和数据挖掘技术，采用请求响应方式以及规范化的接口结构，提出一个企业信息分析及决策系统的设计和解决方案。根据完善的智能化分析手段，有效的保证决策者的经验与决策支持系统的分析和预测能力相结合，并对企业管理者提供科学准确的分析预测和决策支持。系统可实时地为用户提供有效详细的分析数据，从而大大加快决策进程并有效提高准确性。     

自适应神经模糊系统在托砖架最高温度预测中的应用

气流床气化炉在气化工业中得到了广泛应用，托砖架用于支撑气化炉耐火砖的重量，必须有足够的强度，温度是托砖架强度的重要影响因素。传统方法使用有限单元法计算，需要编制复杂的计算机程序。本文使用已有软件计算出托砖架最高温度点温度，对一个自适应神经模糊系统(ANFIS)进行训练，然后对未知数据进行预测，取得了准确的结果。