多目标模糊全局优化设计方法和软件

分析了优化设计在土木工程中应用不够广泛的原因，由此提出了一个能用于工程设计的多目标模糊全局优化设计方法。介绍了按该方法研制的软件ＭＦＤ的功能和性能，以及一个用该软件分析的拱坝体型设计实例。     

球罐活性缺陷的模糊评定方法

根据国内外有关标准和文献,利用模糊数学中的模糊描述和隶属度等概念,首次采用多级模糊综合评定理论,分层次地把活性缺陷的主要监测信息(即声发射源信息)和专家们的经验考虑在内,建立在用球形贮罐声发射源严重度多级模糊综合评定模型和方法,并编制计算机数据分析和处理程序,使评定结果趋于更合理和可靠.并给出在用球形贮罐声发射源严重度多级模糊综合评定算例,结果表明,该评定方法可以达到定量化的程度,与局部无损复验结果有良好的一致性.     

模糊Petri网及知识表示

在建造专家系统中虽然有很多种知识表示方法，但都有不尽人意的地方，本文试图用一种新的方法－Ｐｅｔｒｉ网模型来表示知识。本文给出了Ｆｕｚｚｚｙ Ｐｅｔｒｉ网和广义Ｆｕｚｚｙ Ｐｅｔｒｉ网两种模型，并给出了相应的推理算法，一旦专家知识用Ｐｅｔｒｉ网表示后，根据Ｐｅｔｒｉ网固有的特性，我们就能处理专家系统中并行推理、无回溯推理，反向推理等问题。     

大坝施工混凝土质量的评价体系

本文介绍了大坝施工混凝土质量的评价系统软件的设计思路和设计成果。它利用现代计算机技术和应用模糊数学理论评价混凝土重力坝和拱坝单块和整体的质量。在采用模糊综合评价工程质量中，对不同工艺环节，对坝体不同部位，鉴于其质量要求的差异应赋予不同的权重，在权重的取值上采用了专家调查排序，定量转化的方法是比较适宜的。在程序设计中，评价数学模型采用了BASIC程序，访膜块通过*TXT文件实现了与整个大坝混凝土质量微机管理系统的接口及数据传递。操作运行方便灵活。     

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.     

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.