基于模糊逻辑的连续滑模控制

针对一类具有不确定性的非线性系统，根据滑模控制原理，提出了一种基于模糊逻辑的连续滑模控制设计方法。由于使用了适当的模糊逻辑切换，避免了滑模控制所固有的颤动现象．仿真结果表明，本文设计的模糊控制，对模型不确定性和外来干扰具有较强的鲁棒性和良好的跟踪性能．     

基于组件的决策支持系统模型设计与实现

决策支持系统（Decision Support System简称DSS）是模型驱动的，DSS中模型的设计与实现是一个有待进一步研究的问题。本文把组件技术引入到DSS模型的设计与实现中来，提出基于组件技术的DSS的模型设计与实现策略。给出基于组件思想的DSS模型定义和运算，并给出基于组件技术的DSS基模型和复合模型的实现方法。     

基于H∞滤波器的不确定状态时滞系统鲁棒故障诊断

针对一类具有未知输入和模型不确定性的动态时滞系统，基于H∞滤波器技术研究了系统的鲁棒故障检测问题．利用H∞控制理论得到了系统的故障检测滤波器设计方法，证明了故障检测残差对不确定性具有范数界的鲁棒性；然后利用线性矩阵不等式（LMI）技术来求解此时滞独立H∞滤波器设计问题，并给出了该滤波器解存在的条件以及滤波器增益矩阵的求解方法．最后，通过一个仿真例子验证了本方法的有效性．     

基于直接状态空间理论的H∞鲁棒控制器设计方法及实例

鲁棒性是系统的重要性能指标。H∞鲁棒控制能够解决系统存在不确定性时的鲁棒性问题。一般的H∞鲁棒控制器设计方法的计算量偏大。基于直接状态空间理论，通过无迭代的求解两个Riccati方程得到具有鲁棒性的控制器，从而解决了计算量偏大的问题。最后讨论了H∞控制理论的应用。     

基于神经网络的多变量发酵过程自适应控制

运用非线性系统的线性化方法与神经网络在线辨识技术，提出了一种基于神经网络 的多变量自适应控制策略．提出的控制策略，当过程模型缺乏足够的先验知识时，对多变量 非线性连续发酵过程取得了良好的控制性能．仿真结果表明，提出的自适应控制方法能够适 应过程模型的不确定性和参数的时变性，具有较强的鲁棒性．并且通过对比分析得出，基于 微分几何理论的输入输出线性化解耦控制方案，由于控制器的设计依赖于过程模型，对模型 参数的变化很敏感，应用在发酵过程的非线性控制中，控制精度较低，鲁棒性较差．     

一类不确定离散时间系统的鲁棒故障诊断滤波器优化设计方法

研究线性不确定离散时间系统的鲁棒故障诊断滤波器设计问题。基于新提出的性能指标函数，将不确定离散时间系统的故障诊断漶波器设计问题归结为肌优化问题，并通过选择适当的后滤波器和观测器增益矩阵，得到未知输入和模型不确定性鲁棒性的最优解。     

Globus支持下基于网格的开放式决策支持系统设计与实现

网格技术的出现和成熟将给决策支持系统(DSS)带来一场新的变革．提出了一种新型的DSS——基于网格的开放式决策支持系统(GBODSS)，它充分地利用了网格技术的优势，将网格技术和DSS结合在一起，并给出了该框架利用Globus工具的一种实现方案．     

基于模糊逻辑的滑模控制设计方法

针对一类具有不确定性的单输入非线性系统，根据滑模控制原理。采用趋近律方法优化的滑模控制设计方法，以满足控制系统的鲁棒性和趋近运动品质的要求；同时根据模糊理论将切换函数模糊化，并且通过模糊规则自适应调整切换函数的模糊量化因子，以消除变结构控制系统固有的抖振和进一步改善系统偏差。仿真结果表明．设计的模糊滑模系统，具有对模型不确定性和外来干扰较强的鲁棒性以及良好的跟踪性能。避免了系统固有的颤动现象，控制的效果良好，性能满意；可应用于此类不确定非线性系统的设计综合问题。     

ATM 网络预测拥塞控制器设计

网络传输中存在严重的不确定性，由此限制了常规反馈拥塞控制算法的应用．利用预测控制方法，设计出一种改进的拥塞控制算法，增强了闭环系统的鲁棒性和稳定性，实现了带宽分配的公平性．仿真结果证实了所提出方法是有效性的。     

网络控制系统的μ鲁棒控制研究

基于结构奇异值理论,针对网络控制系统的延时问题,建立了网络控制系统的鲁棒主动控制模型,提出了μ鲁棒控制器的设计方法。通过引入虚拟不确定块将网络控制系统的鲁棒性问题转化为广义系统的鲁棒稳定性问题求解。仿真表明,μ综合方法对系统模型不确定性具有很好的稳定鲁棒性和性能鲁棒性。     

Cognitive biases and decision support systems development: a design science approach

Abstract.  This paper presents design science research that aims to improve decision support systems (DSS) development in organizations. Evolutionary development has been central to DSS theory and practice for decades, but a significant problem for DSS analysts remains how to conceptualize the improvement of a decision task during evolutionary DSS development. The objective of a DSS project is to improve the decision process and outcome for a manager making an important decision. The DSS analyst needs to have a clear idea of the nature of the target decision task and a clear strategy of how to support the decision process. Existing psychological research was examined for help with the conceptualization problem, and the theory of cognitive bias is proposed as a candidate for this assistance. A taxonomy of 37 cognitive biases that codifies a complex area of psychological research is developed. The core of the project involves the construction of a design artefact – an evolutionary DSS development methodology that uses cognitive bias theory as a focusing construct, especially in its analysis cycles. The methodology is the major contribution of the project. The feasibility and effectiveness of the development methodology are evaluated in a participatory case study of a strategic DSS project where a managing director is supported in a decision about whether to close a division of a company.     

A decision support system for the bimodal dial-a-ride problem

A bimodal dial-a-ride problem (BDARP) considered in this paper is a dial-a-ride problem that involves two transportation modes: paratransit vehicles and fixed route buses. Riders in such a system might be transferred between different transportation modes during the service process. The motivation of this research is that by efficiently coordinating paratransit vehicles with fixed route buses we can improve the accessibility and efficiency of a dial-a-ride system. In this paper, we design a decision support system (DSS) which automatically constructs efficient paratransit vehicle routes and schedules for the BDARP. This DSS has been tested using actual data from the Ann Arbor Transportation Authority (AATA) in Ann Arbor, MI. The results show that this DSS produces an average increase of 10% in the number of requests that can be accommodated and an average decrease of 10% in the number of paratransit vehicles required, as compared to the manual results where no fixed route buses are involved     

基于Web和组件技术的DSS模型设计与实现

决策支持系统(DSS)主要用于帮助企业的领导者进行事件决策和趋势分析。该文将组件技术引入到基于B／S结构的DSS设计中,提出了基于Web技术和组件技术的DSS模型设计与实现策略,并结合某银行基于B／S的决策支持系统实例,具体阐述了模型设计思想和实现步骤。     

Decision support system development for integrated management of European coastal lagoons

In this paper, a general framework for the development of Decision Support Systems (DSSs) for the management of coastal lagoons is presented. The proposed DSS structure integrates the information provided by several models accounting for different characteristics of lagoon ecosystems, including biogeochemical, hydrodynamic, ecological and socio-economic aspects. Outputs and indicators provided by the models are used to accomplish the decision task by the application of multicriteria analysis. Model uncertainty and robustness with respect to uncontrollable factors are addressed. Application of the proposed DSS structure to five lagoons located in the Mediterranean area is discussed, with special focus on the management of clam farming in the Sacca di Goro lagoon (Italy). Thanks to its flexibility, the proposed DSS structure is also applicable in decision problems arising in different fields.     

Regret avoidance as a measure of DSS success: An exploratory study

Assessing the value of decision support systems (DSS) is an important line of research. Traditionally, researchers adopt user satisfaction and decision performance to measure DSS success. In some cases, however, the use of DSS is not benefit driven. Instead, DSS adoption may be motivated by avoiding decision errors or reducing decision cost, indicating that regret avoidance may be a useful measure of DSS success. Regret is a post-decision feeling regarding not having chosen a better alternative. Recent behavioral research has indicated that, in addition to pursuing higher performance and user satisfaction, reducing decision regret is another important consideration for many decision-makers. This exploratory study extends prior research on DSS evaluation by proposing regret avoidance as an additional measure of DSS success. Experimental results regarding the use of DSS for stock investment demonstrate DSS use significantly reduces regret in situations involving low user satisfaction. Consequently, besides decision performance and user satisfaction, regret reduction is also important in measuring the effectiveness of DSS.     

A conceptual architecture for DSS generators

The development of DSS generators is a complicated task. No existing DSS generator has been reported as a generalised, powerful and “user friendly” system, which provides full-range of capabilities for easily building specific DSS in any application area. In order to integrate a variety of decision support and data management capabilities into a well-designed, orderly whole, a conceptual design model must be created as a foundation for developing such software systems. Sprague's DSS model provides a basis for the creation of a foundation for DSS generator development. This paper proposes a comprehensive conceptual design model which is an in-depth augmentation of Sprague's original model. This model not merely provides a basis for developing DSS generators; it also proposes a fundamental architecture of DSS generators which removes some of the responsibilities of DSS design from the user. Further, the conceptual model has been technically validated by implementing an experimental DSS generator REGIMES on a microcomputer. This implementation also demonstrates the feasibility of implementing a powerful DSS generator on cost-effective hardware.     

Decision support systems: An expert systems approach

This paper provides a conceptual framework for designing decision support systems (DSS) using an expert systems approach. Currently there is a significant trend towards the use of knowledge-based systems techniques in DSS design, but a comprehensive framework is yet to be proposed. Our paper addresses this problem and presents such a framework. Efforts are currently underway to design, implement and test a system based on this framework.     

A new approach to water quality modelling and environmental decision support systems

The concepts and technology of environmental decision support systems (EDSS) have developed considerably over recent decades, although core concepts such as flexibility and adaptability within a changing decision environment remain paramount. Much recent EDSS theory has focussed on model integration and re-use in decision support system (DSS) tools and for design and construction of ‘DSS generators’. Many current specific DSS have architectures, tools, models and operational characteristics that are either fixed or difficult to change in the face of changing management needs. This paper reports on development and deployment of an EDSS that encompasses a new approach to DSS tools, generators and specific DSS applications. The system, named E2, is built upon a conceptualisation of terrestrial and aquatic environmental systems that has resulted in a robust and flexible system architecture. The architecture provides a set of base classes to represent fundamental concepts, and which can be instantiated and combined to form DSS generators of varying complexity. A DSS generator is described within which system users are able to select and link models, data, analysis tools and reporting tools to create specific DSS for particular problems, and for which new models and tools can be created and, through software reflection (introspection), discovered to provide expanded capability where required. This system offers a new approach within which environmental systems can be described in the form of specific DSS at a scale and level of complexity suited to the problems and needs of decision makers.     

A structured modeling based methodology to design decision support systems

Many Decision Support Systems (DSS) support the decision making process through the use of mathematical models and data. DSS design involves modeling data as well as mathematical relationships in a domain. The process of model formulation and subsequent integration of model with data in a DSS is a complex and ill-structured process. This paper proposes a methodology based on Structured Modeling (SM), originally introduced by Geoffrion together with the modeling language SML, to model and design the DSS. The methodology includes rigorous and step by step procedures to design and integrate data and modelbases. The main contribution of our approach lies in the integration of research in database design, and mathematical model formulation within the structured modeling framework. The resultant procedures can be easily automated and taught to students in DSS courses. The motivation for our research stemmed from our constant frustrations in teaching DSS courses over the last five years. In the last two years, when we used our methodology, the performance of the students improved significantly. The average score in the DSS project went up to 85 from 60. Our positive experience in using our methodology in classes over the past two years suggests that the methodology imposes structure into the analysis of decision problems, and as a result students produce better DSS designs for classroom cases.