A fuzzy data envelopment analysis approach for FMEA

We present a data envelopment analysis approach for determining ranking indices among failure modes in which the typical FMEA parameters are modeled as fuzzy sets. By this approach, inference rules of the IF THEN kind can be bypassed. The proposed approach is applied to a typical PWR auxiliary feedwater system. The results are compared to those obtained by means of: the risk priority numbers, pure fuzzy logic concepts, and finally the DEA-APGF (profiling of severity efficiency) approach. The results demonstrate the potential of the combination of fuzzy logic concepts and data envelopment analysis for this class of problems.     

Fuzzy logic control of vehicle suspensions with dry friction nonlinearity

We design and investigate the performance of fuzzy logic-controlled (FLC) active suspensions on a nonlinear vehicle model with four degrees of freedom, without causing any degeneration in suspension working limits. Force actuators were mounted parallel to the suspensions. In this new approach, linear combinations of the vertical velocities of the suspension ends and accelerations of the points of connection of the suspension to the body have been used as input variables. The study clearly demonstrates the effectiveness of the fuzzy logic controller for active suspension systems. Suspension working space degeneration is the most important problem in various applications. Decreasing the amplitudes of vehicle body vibrations improves ride comfort. Body bounce and pitch motion of the vehicle are presented both in time domain when travelling over a ramp-step road profile and in frequency domain. The results are compared with those of uncontrolled systems. At the end of this study, the performance and the advantage of the suggested approach and the improvement in ride comfort are discussed.     

Classification of heart rate data using artificial neural network and fuzzy equivalence relation

The electrocardiogram is a representative signal containing information about the condition of the heart. The shape and size of the P-QRS-T wave, the time intervals between its various peaks, etc. may contain useful information about the nature of disease afflicting the heart. However, these subtle details cannot be directly monitored by the human observer. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the signal parameters, extracted and analysed using computers, are highly useful in diagnostics. This paper deals with the classification of certain diseases using artificial neural network (ANN) and fuzzy equivalence relations. The heart rate variability is used as the base signal from which certain parameters are extracted and presented to the ANN for classification. The same data is also used for fuzzy equivalence classifier. The feedforward architecture ANN classifier is seen to be correct in about 85% of the test cases, and the fuzzy classifier yields correct classification in over 90% of the cases.     

锅炉燃烧器模糊-PID复合控制器的设计

将模糊-PID复合控制器应用于锅炉燃烧器控制器的设计中，许用C8051F020 MCU及其外部接口电路来实现控制。从仿真结果表明，使用这种模糊-PID复合控制器可以获得很好的控制效果。     

Practical implementation of possibilistic probability mass functions

 Probability assessments of events are often linguistic in nature. We model them by means of possibilistic probabilities (a version of Zadeh's fuzzy probabilities with a behavioural interpretation) with a suitable shape for practical implementation (on a computer). Employing the tools of interval analysis and the theory of imprecise probabilities we argue that the verification of coherence for these possibilistic probabilities, the corrections of non-coherent to coherent possibilistic probabilities and their extension to other events and gambles can be performed by finite and exact algorithms. The model can furthermore be transformed into an imprecise first-order model, useful for decision making and statistical inference.     

一种自适应模糊串级控制仿真

模糊控制的基本思想就是利用计算机来实现人的定性控制经验。该文考虑了参数变化和干扰的情况 ,并与传统的方法作了比较 ,证明使用模糊串级系统具有良好的阶跃响应品质 ,并具有较好的自适应效果。     

The uses of fuzzy logic in autonomous robot navigation

 The development of techniques for autonomous navigation in real-world environments constitutes one of the major trends in the current research on robotics. An important problem in autonomous navigation is the need to cope with the large amount of uncertainty that is inherent of natural environments. Fuzzy logic has features that make it an adequate tool to address this problem. In this paper, we review some of the possible uses of fuzzy logic in the field of autonomous navigation. We focus on four issues: how to design robust behavior-producing modules; how to coordinate the activity of several such modules; how to use data from the sensors; and how to integrate high-level reasoning and low-level execution. For each issue, we review some of the proposals in the literature, and discuss the pros and cons of fuzzy logic solutions. Received: 31 March 1997 / Accepted: 24 September 1997     

并行遗传算法与神经网络,模糊系统的结合

遗传算法是模拟自然界生物进化过程的计算模型。本文介绍了并行遗传算法的不同分类及不同并行策略，又将遗传算法分别与神经网络、模糊系统结合起来进行并行处理，并在曙光１０００系统上实现。算法分析表明，并行遗传算法可以有效地提高收敛速度。     

复杂系统的广义预测模糊控制

针对多变量复杂系统提出一种基于广义预测与模糊控制相集成的控制策略。控制器具有多层结构与智能控制特点，不仅计算量小、实时性强，而且具有良好的“解耦”和动态跟踪性能，明显地提高了该类系统的闭环控制效果。     

泥沙随机起动的简化模式

泥沙的起动既有随机性，又有模糊性，据此作者将模糊数学方法引入泥沙起动研究。建立了直接反映泥沙随机起动特性的起动流速计算模式，该模式得到了实测资料的检验。这一模式可以体现泥沙起动的随机性，较好地反映了泥沙起动的本来面目