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。     

一种基于GE90-30PLC的飞剪自动控制系统

本文介绍了调整线材轧线精轧机前专用于切头的启停式飞剪的自动控制系统。该系统以GE90—30PLC为控制核心，辅以热金属HMD、光电编码器等元件，能很好地完成飞剪的剪切及定位。其中着重阐述了飞剪控制中各个重要参数的计算原理和方法。     

运用模糊综合评判方法定量研究断层封闭性

综合考虑影响断层封闭性的7种主要因素，运用模糊评判方法对夏口断裂带中部3条主要控油断层的封闭性进行了定量研究。综合评价结果与实际勘探相符，表明评判方法是可靠的，可为工区的油气勘探提供科学依据。     

Fodor and Pylyshyn on connectionism

Fodor and Pylyshyn (1988) have argued that the cognitive architecture is not Connectionist. Their argument takes the following form: (1) the cognitive architecture is Classical; (2) Classicalism and Connectionism are incompatible; (3) therefore the cognitive architecture is not Connectionist. In this essay I argue that Fodor and Pylyshyn's defenses of (1) and (2) are inadequate. Their argument for (1), based on their claim that Classicalism best explains the systematicity of cognitive capacities, is an invalid instance of inference to the best explanation. And their argument for (2) turns out to be question-begging. The upshot is that, while Fodor and Pylyshyn have presented Connectionists with the important empirical challenge of explaining systematicity, they have failed to provide sufficient reason for inferring that the cognitive architecture is Classical and not Connectionist.     

Self-learning inductive inference machines

Self-knowledge is a concept that is present in several philosophies. In this article, we consider the issue of whether or not a learning algorithm can in some sense possess self-knowledge. The question is answered affirmatively. Self-learning inductive inference algorithms are taken to be those that learn programs for their own algorithms, in addition to other functions.
La connaissance de soi est un concept qui se retrouve dans plusieurs philosophies. Dans cet article, les auteurs s'interrogent à savoir si un algorithme d' apprentissage peut dans une certaine mesure posséder la connaissance de soi. lis apportent une reponse positive a cette question. Les algorithmes d'inference inductive autodidactes sont ceux qui font l'apprentissage de programmes pour leurs propres algorithmes, en plus d' autres fonctions.     

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。