钢铁企业价格预测决策模型体系研究

通过对钢铁企业现行价格管理体系的分析 ,建立了钢铁企业价格预测决策模型体系。充分利用大量的定性、定量信息 ,并把趋势预测和因果预测技术相结合 ,采用组合预测模型对价格进行预测 ,最后对预测出来的多方案进行模糊综合评价 ,给出推荐方案顺序。实际应用表明 ,模型体系是有效的。     

Rule-based supervisory control of a two-link flexible manipulator

In this paper, we propose a two level hierarchical control strategy to achieve accurate end-point position of a planar two-link flexible manipulator. The upper level consists of a feedforward rule-based supervisory controller that incorporates fuzzy logic, whereas the lower level consists of conventional controllers that combine shaft position-endpoint acceleration feedback for disturbance rejection properties and shaping of the (joint) actuator inputs to minimize the energy transferred to the flexible modes during commanded movements. The effectiveness of this hierarchical control strategy is verified by experimental results for various movements of the links, in various configurations. In particular, we illustrate how the hierarchical intelligent control strategy performs better than conventional control techniques for endpoint position control in the presence of flexure effects.     

基于Apriori算法改进的关联规则提取算法

通过对Apriori算法的基本思想和性能的研究分析，认为Apriori算法存在一些不足。并且根据这些不足提出了相应的改进算法对Apriori算法进行优化，从而得到一种改进的Apriori算法，与原算法相比运算效率大大提高。     

动态模糊神经网络在大坝变形预报中的应用

针对静态模糊神经网络的局限性,提出了在线动态建模的模糊神经网络方法.当新增样本进入训练集之后,根据新样本对模型的贡献大小,在已有模型的基础上进行动态修正,这样可以减少建模的计算时间.新方法实现了增加样本而矩阵阶数不增加,避免了矩阵求逆运算,理论上可以提高计算效率.实例表明动态模糊神经网络方法是可行的,可实现持久预报,具有较强的适应能力和较高的预报精度,可应用于在线实时变形预报及相关领域.     

模糊规划在炼油厂生产计划优化中的应用

生产计划的编制是炼油化工企业生产经营管理中的一个关键环节，然而由于市场变化的不确定性，使得企业的长远规划制定受到限制，笔者将模糊数学应用于炼厂生产计划的编制，通过模糊规划对炼油企业生产计划进行优化，使编得的生产计划在一定程度上反映市场的变化并使企业的生产效益最优，为企业管理决策者进行长期规划给予指导，给出了实例来说明该方法的可行性。     

A genetic‐based neuro‐fuzzy controller for blind equalization of time‐varying channels

This paper presents a neuro‐fuzzy network (NFN) where all its parameters can be tuned simultaneously using genetic algorithms (GAs). The approach combines the merits of fuzzy logic theory, neural networks and GAs. The proposed NFN does not require a priori knowledge about the system and eliminates the need for complicated design steps such as manual tuning of input–output membership functions, and selection of fuzzy rule base. Although, only conventional GAs have been used, convergence results are very encouraging. A well‐known numerical example derived from literature is used to evaluate and compare the performance of the network with other equalizing approaches. Simulation results show that the proposed neuro‐fuzzy controller, all parameters of which have been tuned simultaneously using GAs, offers advantages over existing equalizers and has improved performance. From the perspective of application and implementation, this paper is very interesting as it provides a new method for performing blind equalization. The main contribution of this paper is the use of learning algorithms to train a feed‐forward neural network for M‐ary QAM and PSK signals. This paper also provides a platform for researchers of the area for further development. Copyright © 2008 John Wiley & Sons, Ltd.     

Data mining of resilience indicators

In recent years, the Asia-Pacific region has experienced several financial setbacks, including speculative attacks in 1998 and the SARS outbreak in 2003. Financial stresses of this nature are unanticipated, and not all of the dangers can be predicted by the examination of market information and macroeconomic indicators. The Early Warning System (EWS) that has been adopted by the International Monetary Fund may not be able to predict future financial crises for all possible scenarios, because shocks come in many different forms. To supplement the EWS, this paper proposes a data mining framework to measure the resilience of an economy. The resilience framework does not predict a crisis, but rather assesses the current state of health of an economy and its ability to withstand a financial shock should one occur. The framework is based on a feedback system consisting of two stages. The first stage assigns a resilience score to each economy based on a fuzzy logic scoring scheme that is built on the ambiguous reasoning of experts. The second stage uses the classification tree approach to estimate thresholds for each economic indicator, and examines the quality of the fuzzy score. The result from the second stage is then passed back to the first stage as feedback. The final result is obtained when the feedback system reaches its equilibrium state. The proposed resilience framework is applied to the external-sector and the public-sector economies of several countries to illustrate its applicability.     

基于模糊信息处理的中板轧机微机在线监测系统

中板轧机是中板厂最重要的生产设备，对其工况进行在线监测意义重大，各种传感器将轧机的工况信息传送给微机系统，微机系统将这些信息进行模糊处理，发出相关信息，指导系统安全，高效运行。     

基于模糊理论的江垭水库移民安置区模式优选

水库移民安置区的优化选择关系到移民社区未来的社会稳定和可持续发展。为此,对影响移民安置区的指标体系进行了研究,利用模糊理论建立了优选模型,并通过江垭水库移民安置区的实际情况对模型进行了验证。结果表明,研究采用的指标体系和方法可满足移民安置区优选的需要。     

用模糊综合评判法评估钢制储罐的寿命

针对影响钢制储罐寿命的因素具有复杂性、非确定性以及时变性的特点,提出了用动态二级模糊综合评判的方法对其寿命进行评估,建立了评估的数学模型和评估流程。在对某石油库油罐的寿命分析中,考虑到保留所有评判因素的影响,采用了乘、加型模糊算子,并用层次分析法来确定评价指标权重。计算结果表明,该方法能够解决储罐寿命评估中的一些复杂因素的处理问题,具有一定的实际工程意义。