Applications of fuzzy sets and approximate reasoning

This paper discusses recent applications of fuzzy sets and the theory of approximate reasoning. The primary focus is on fuzzy logic control (FLC). We begin with a brief history of the key ideas, a survey of recent applications, and a discussion of the genesis of FLC in Japan. We then turn to a study of the general principles of FLC, considering it as a combination of ideas from conventional control theory, artificial intelligence, and fuzzy sets theory. We next provide a detailed analysis of a simple application in consumer electronics, namely, a fuzzy washing machine developed by Hitachi Corporation. In concluding sections we briefly consider other types of applications, including recent work on pilotless helicopters, fuzzy expert systems, and the concept of a fuzzy computer, and we discuss the potential for future developments. It is our opinion that the subject of FLC is still very much in its infancy, and that recent events mark the beginning of an entirely new genre of “intelligent” control     

Handoff algorithms based on fuzzy classifiers

One method to increase capacity in mobile communications systems is to decrease the cell size or service area. However, due to this area reduction, a mobile will pass through more cells during a call. To maintain the reliability of the system, the success rate of call transfers must increase as the cell area diminishes, preventing a ping-pong effect. Hence new and better handoff algorithms must be developed. Fuzzy classifier schemes are proposed in this paper in order to select the base stations that will best serve mobiles in a typical cellular system. The performance of handoff algorithms based on different classification techniques is discussed and evaluated through digital simulation. The results show better cell definition than the conventional handoff approach. For large-variance propagation channels, a reduction in the handoff number can be achieved. The applicability of the proposed methodology is also discussed     

Defuzzification method for multishaped output fuzzy sets

A defuzzification method is an important performance factor in fuzzy logic controllers (FLCs). When an FLC uses multishaped output fuzzy sets, existing defuzzification methods have the fat shape dominance phenomenon. The authors propose a new defuzzification method for multishaped output fuzzy sets. The method takes the degree of certainty on the fired level into account in the defuzzification process. Simulation experiments with a nonlinear plant show that an FLC employing the proposed defuzzification method is more stable and robust than existing FLCs     

The implementation of fuzzy algorithms for control

This letter describes the implementation of control systems designed using fuzzy algorithms and fuzzy logic. The resulting technique employs lookup tables. An assessment is made of the suitability of such tables, particularly when applied to short-word-length digital control systems. An interpreter program which can be used to generate the required lookup tables is also described.     

Evolutionary algorithms for fuzzy control system design

This paper provides an overview on evolutionary learning methods for the automated design and optimization of fuzzy logic controllers. In a genetic tuning process, an evolutionary algorithm adjusts the membership functions or scaling factors of a predefined fuzzy controller based on a performance index that specifies the desired control behavior. Genetic learning processes deal with the automated design of the fuzzy rule base. Their objective is to generate a set of fuzzy if-then rules that establishes the appropriate mapping from input states to control actions. We describe two applications of genetic-fuzzy systems in detail: an evolution strategy that tunes the scaling and membership functions of a fuzzy cart-pole balancing controller and a genetic algorithm that learns the fuzzy control rules for an obstacle-avoidance behavior of a mobile robot     

基于模糊和遗传算法的阈值分割方法

提出了一种阈值分割方法。本算法将模糊集理论和遗传算法有机地结合起来,它是一种有监督的分割方法,首先确定图像阈值的数目,然后将灰度图像模糊化,确定准则函数,采用遗传算法求得意了优解,最后经过后处理得到最终分割结果。实验表明该方法运算速度快,且对噪声不敏感,具有较高的鲁棒性。     

Image segmentation by histogram thresholding using fuzzy sets

Methods for histogram thresholding based on the minimization of a threshold-dependent criterion function might not work well for images having multimodal histograms. We propose an approach to threshold the histogram according to the similarity between gray levels. Such a similarity is assessed through a fuzzy measure. In this way, we overcome the local minima that affect most of the conventional methods. The experimental results demonstrate the effectiveness of the proposed approach for both bimodal and multimodal histograms.     

基于模糊集的图像增强算法研究

该文通过定义新的隶属函数形式提出了一种自动图像增强的方法,该方法将图像转化为等效的图像模糊特征平面,在此基础上进行模糊增强,最后再转换为空域图像;模糊增强提高了低灰度区域和高灰度区域之间的对比度,从而使增强后图像的效果更好;最后对具有多峰直方图分布图像的模糊增强方法进行了推广。     

Adaptive fuzzy sliding mode active queue management algorithms

Active queue management (AQM) is aimed at achieving the tradeoff between link utilization and queuing delay to enhance TCP congestion control and is expected to perform well for a wider-range of network conditions. Static AQM schemes despite their simplicity, often suffer from long response time due to conservative parameter setting to ensure stability. Adaptive parameter settings, which might solve this problem, remain difficult from implementation point of view. In this paper, we propose an adaptive fuzzy sliding mode (AFSM) AQM algorithm to achieve fast response and yet good robustness. The AFSM algorithm uses the queue length and its differential as the input of AQM and adjusts fuzzy rules by the measurement of packet loss ratio dynamically. The stability analysis under heterogeneous round trip times provides guidelines for parameter settings in AFSM and guarantees that the stability of AFSM is independent of the active TCP flows. This merit as well as other performances is examined under various network environments. Compared to some typical AQMs, the AFSM algorithm trades off the throughput with queuing delay better and achieves a higher per-flow throughput. Finally, AFSM can be executed at a scale of seconds with the least fuzzy rules.     

Optimization of fused fuzzy systems via genetic algorithms

In this paper, the problem of optimization of fused fuzzy systems via genetic algorithms is addressed. The proposed architecture separates a large fuzzy rule optimization problem into a series of more manageable and smaller problems through rule-table transformation. The algorithm is applied to the cart-pole system to demonstrate its characteristics     

Applications of fuzzy sets to rule-based expert system development

In this paper, problems of implementing rule-based expert systems using fuzzy sets are considered. A fuzzy logic software development shell is used that allows inclusion of both crisp and fuzzy rules in decision making and process control problems. Results are given that compare this type of expert system to a human expert in some specific applications. Advantages and disadvantages of such systems are discussed.     

基于Vague集的动态多目标模糊决策方法

提出了一种基于Vague集的动态多目标模糊决策方法。根据支持（反对、中立）目标集的定义,将单时段中的各方案表示成一个Vague值,各个时段的Vague值即构成了方案的Vague集描述,通过定义理想最优方案,利用Vague集之间的加权相似度量,计算各方案Vague集与理想最优方案Vague集的相似度,按照相似度的大小对方案进行排序,从而选出最优方案。算例验证了方法的有效性。     

Describing fuzzy sets using a new concept: fuzzify functor

This paper proposed a fuzzify functor as an extension of the concept of fuzzy sets.The fuzzify functor and the first-order operated fuzzy set are defined.From the theory analysis,it can be observed that when the fuzzify functor acts on a simple crisp set,we get the first order fuzzy set or type-1 fuzzy set.By operating the fuzzify functor on fuzzy sets,we get the higher order fuzzy sets or higher type fuzzy sets and their membership functions.Using the fuzzify functor we can exactly describe the type-1 fuzz...     

一种新的基于直觉模糊集的作战决策算法

针对现有作战决策算法存在的不足,提出了一种新的基于直觉模糊集的作战决策算法.用直觉模糊值表示可行方案指标的不确定信息,综合考虑了方案满足指标的可能性、不满足的可能性和未知的可能性3个方面的因素,给出了直觉模糊决策矩阵的建立方法和可行方案优劣度排序方法.实例及实际应用证明了算法的有效性与可行性.     

A fuzzy logic classification scheme for selecting and blendingsatellite ocean color algorithms

An approach for selecting and blending bio-optical algorithms is demonstrated using an ocean color satellite image of the northwest Atlantic shelf. This approach is based on a fuzzy logic classification scheme applied to the satellite-derived water-leaving radiance data, and it is used to select and blend class-specific algorithms. Local in situ bio-optical data were used to characterize optically-distinct water classes a priori and to parameterize algorithms for each class. Although the algorithms can be of any type (empirical or analytical), this demonstration involves class-specific semi-analytic algorithms, which are the inverse of a radiance model. The semi-analytic algorithms retrieve three variables related to the concentrations of optically active constituents. When applied to a satellite image, the fuzzy logic approach involves three steps. First, a membership function is computed for each pixel and each class. This membership function expresses the likelihood that the measured radiance belongs to a class, with a known reflectance distribution. Thus, for each pixel, class memberships are assigned to the predetermined classes on the basis of the derived membership functions. Second, three variables are retrieved from each of the class-specific algorithms for which the pixel has membership. Third, the class memberships are used to weight the class specific retrievals to obtain a final blended retrieval for each pixel. This approach allows for graded transitions between water types, and blends separately tuned algorithms for different water masses without suffering from the “patchwork quilt” effect associated with hard-classification schemes     

Volume delineation by fusion of fuzzy sets obtained frommultiplanar tomographic images

Techniques of three-dimensional (3-D) volume delineation from tomographic medical imaging are usually based on 2-D contour definition. For a given structure, several different contours can be obtained depending on the segmentation method used or the user's choice. The goal of this work is to develop a new method that reduces the inaccuracies generally observed. A minimum volume that is certain to be included in the volume concerned (membership degree mu = 1), and a maximum volume outside which no part of the volume is expected to be found (membership degree mu = 0), are defined semi-automatically. The intermediate fuzziness region (0 < mu < 1) is processed using the theory of possibility. The resulting fuzzy volume is obtained after data fusion from multiplanar slices. The influence of the contrast-to-noise ratio was tested on simulated images. The influence of slice thickness as well as the accuracy of the method were studied on phantoms. The absolute volume error was less than 2% for phantom volumes of 2-8 cm3, whereas the values obtained with conventional methods were much larger than the actual volumes. Clinical experiments were conducted, and the fuzzy logic method gave a volume lower than that obtained with the conventional method. Our fuzzy logic method allows volumes to be determined with better accuracy and reproducibility.     

遗传算法在雷达组网最优化布阵中算法研究

分析了遗传算法用于雷达组网最优化布阵的基本原理和关键技术。遗传算法是从多个初始点开始寻优，通过判断适度函数决定是否被淘汰或选择，从而实现全局或准全局最优，与传统雷达组网的效用函数法、专家法等方法比较具有原理简单、搜索量少、速度快等优点，解决了传统算法搜索量大、速度慢等问题。计算机的仿真实验表明了该方法的有效性。