Parallel, high-speed PC fuzzy control

To overcome the inefficiencies in processing fuzzy rules on sequential digital computers and the inflexibility of purely analog processors, we introduce a parallel architecture using analog processors with a digital interface. Our architecture for fuzzy processing supports ISA-based PC platforms. Single or multiple fuzzy units with analog processing cores can operate as stand-alone MISO or MIMO fuzzy logic controllers supporting a digital interface with a master PC (for setup, monitoring, and/or relational MIMO support). The journal issue contains a concise summary of this article. The complete article is linked to Micro's home page on the World Wide Web (http://www.computer.org/pubs/micro/micro.htm)     

A fuzzy partitioning system

Our system uses fuzzy logic to bipartition cells in VLSI circuits. The system uses fuzzy logic principles to generate net and cell indices and a bottom-up clustering algorithm to produce the two-way partition. We tested our algorithm on eleven ISCAS89 benchmark circuits and compare its performance with that of other implementations. The journal issue contains a concise summary of this article. The complete article is linked to Micro's home page on the World Wide Web (http://www.computer.org/pubs/micro/micro.htm)     

A fuzzy controller with an optimized defuzzification algorithm

Our fuzzy controller incorporates generic fuzzy set representations that improve information processing speed. It features antecedent labels represented by trapezoids and an algorithm that accelerates defuzzification by avoiding division. Our processor's performance depends on the centroid's location in the consequent domain: it does better when the centroid is closer to the domain's center, which is the case for many practical control applications. The journal issue contains a concise summary of this article. The complete article is linked to Micro's home page on the World Wide Web (http://www.computer.org/pubs/micro/micro.htm)     

A high-speed, reconfigurable fuzzy logic controller

The modular architecture and reconfigurable inference engine of this analog fuzzy controller offer more flexibility than existing implementations. Its high inference speed and small size make the controller suitable for embedded system applications. The journal issue contains a concise summary of this article. The complete article is linked to Micro's home page on the World Wide Web (http://www.computer.org/pubs/micro/micro.htm)     

Fuzzy min–max neural networks for categorical data: application to missing data imputation

The fuzzy min–max neural network classifier is a supervised learning method. This classifier takes the hybrid neural networks and fuzzy systems approach. All input variables in the network are required to correspond to continuously valued variables, and this can be a significant constraint in many real-world situations where there are not only quantitative but also categorical data. The usual way of dealing with this type of variables is to replace the categorical by numerical values and treat them as if they were continuously valued. But this method, implicitly defines a possibly unsuitable metric for the categories. A number of different procedures have been proposed to tackle the problem. In this article, we present a new method. The procedure extends the fuzzy min–max neural network input to categorical variables by introducing new fuzzy sets, a new operation, and a new architecture. This provides for greater flexibility and wider application. The proposed method is then applied to missing data imputation in voting intention polls. The micro data—the set of the respondents’ individual answers to the questions—of this type of poll are especially suited for evaluating the method since they include a large number of numerical and categorical attributes.     

加权模糊聚集算子研究

在数据挖掘、模糊专家系统和多Agent协同决策过程中,要经常面对信息聚集技术和对多个模糊数据来源进行聚集运算,一般用得最多的是合取、析取及加权平均等算子,但是不同的领域有着不同的需求,本文着重对加权聚集算子进行研究。首先,提出了加权平均关系与析取关系结合后的析取-加权平均算子,该算子解决了加权平均算子不能区分析取与合取的关系。然后,提出了一种最大加权平均算子和最小加权平均算子,该算子将最大最小值算子与加权平均算子进行了泛化,解决了同时考虑信息局部性特征与信息整体性特征的问题。理论分析表明,本文提出的加权模糊聚集算子对于模糊信息源的聚集运算起到了很好的补充和完善的功能。     

An efficient single output fuzzy control algorithm for adaptive applications

A new single output fuzzy control algorithm which is simple and efficient is developed in the paper. Assuming standard type membership functions, a fuzzy set is characterized in this approach by that element in the fuzzy set with maximal membership. Fuzzy rules are then represented by a set of numbers. The algorithm uses a matrix representation of the rules in which the input part of the rule is used as an index, and the indexed element is the corresponding output. This is shown to allow for easy expansion of the output universe. Modification and introduction of new rules are further shown to be very simple and as a salient feature the efficiency of the algorithm is found to increase, when the number of rules increases. This new algorithm is therefore particularly useful and efficient when combined with a rule adaption mechanism. Because of simplicity and efficiency, this algorithm is very suitable for implementation even on small micro processor systems, making adaptive fuzzy control feasible for industrial applications, where a fast and nonlinear control is required.     

Spatial Plateau Algebra for implementing fuzzy spatial objects in databases and GIS: Spatial plateau data types and operations

Many geographical applications have to deal with spatial objects that reveal an intrinsically vague or fuzzy nature. A spatial object is fuzzy if locations exist that cannot be assigned completely to the object or to its complement. Spatial database systems and Geographical Information Systems (GIS) are currently unable to cope with this kind of data. Based on an available abstract data model of fuzzy spatial data types for fuzzy points, fuzzy lines, and fuzzy regions that leverages fuzzy set theory and fuzzy point set topology, this article proposes a Spatial Plateau Algebra that provides spatial plateau data types as an implementation of fuzzy spatial data types. Each spatial plateau object consists of a finite number of crisp counterparts that are all adjacent or disjoint to each other, are associated with different membership values, and hence form different plateaus. The formal framework and the implementation are based on well known, exact models and implementations of crisp spatial data types. Spatial plateau operations as geometric operations on spatial plateau objects are expressed as a combination of geometric operations on the underlying crisp spatial objects. This article offers a conceptually clean foundation for implementing a database extension for fuzzy spatial objects and their operations, and demonstrates the embedding of these new data types as attribute data types in a database schema as well as the incorporation of fuzzy spatial operations into a database query language.     

System reliability for non-repairable multi-state series–parallel system using fuzzy Bayesian inference based on prior interval probabilities

In this article, the fuzzy concepts are applied in analysis of the system reliability problem. The fuzzy number is used to construct the fuzzy reliability of the non-repairable multi-state series–parallel system (NMSS). The fuzzy failure rate function is represented by an exponential fuzzy number. By using this innovative approach, the fuzzy system reliability of NMSS is created. In order to analyse this fuzzy system reliability, the fuzzy Bayesian point estimate of fuzzy system reliability is made by the conventional Bayesian formula. And, the posterior fuzzy system reliability of NMSS is developed by Bayesian inference with fuzzy probabilities. Finally, the performance of the method is measured by the mean square error of fuzzy Bayesian point estimate for the fuzzy system reliability of NMSS.     

Gradual representation of shadowed set for clustering gene expression data

Micro array has been a widely used microscopic measurement that accumulates the expression levels of a large number of genes varying over different time points. Cluster analysis more over the concept of bi-clustering provides insight into meaningful information from the correlation of a subset of genes with a subset of conditions. This eventually helps in discovering biologically meaningful clusters over analyzing missing values, imprecision and noise present in micro array data set. Although the concept of fuzzy set is enough to deal with the overlapping nature of the bi-clusters but the use of shadowed set helps in identifying and analyzing the nature of the genes lying in the confusion area of the clusters. In this article, we have suggested a bi-clustering model of the shadowed set with gradual representation of cardinality and named it as Gradual shadowed set for gene expression (GSS-GE) clustering. It identifies the bi-clusters in the core and in the shadowed region and evaluates their biological significance. The excellence of the proposed GSS-GE has been demonstrated by considering three real data sets, namely yeast data, serum data and mouse data set. The performance is compared with Ching Church’s algorithm (CC), Bimax, order preserving sub matrix (OPSM), Large Average Sub matrices (LAS), statistical plaid model and a modified fuzzy co-clustering (MFCC) algorithm. For the mouse data set there is no cluster level analysis of the micro array has been done so far. We have also provided the statistical and biological significance to prove the superiority of the proposed GSS-GE.     

Use of multi-objective genetic rule selection for examining the effectiveness of inter-vehicle communication in traffic simulations

Recently, inter-vehicle communication (IVC) has been actively studied to attempt to avoid traffic congestion. In this article, we propose the idea of using fuzzy rules to examine the effectiveness of IVC. In the proposed approach, we first collect travel records (e.g., travel time, travel path, traffic volume) of vehicles with IVC from our cellular automata-based traffic simulator. Various kinds of available information for vehicles with IVC are used in the antecedent part of our fuzzy rules. The level of effectiveness of IVC is discretized into four categories (i.e., four classes) in this article. The consequent class of each fuzzy rule is one of those four classes. Next we generate a large number of fuzzy rules from the collected data. Then we select only a small number of fuzzy rules by multi-objective genetic rule selection. We use three objectives: to maximize the accuracy, to minimize the number of selected rules, and to minimize the total rule length (i.e., the total number of antecedent conditions). Our approach can find a number of nondominated fuzzy-rule-based systems with respect to their accuracy and complexity. Finally, we analyze the effectiveness of IVC using fuzzy rules in the fuzzy-rule-based systems obtained through their linguistic interpretation.     

Assessment of micro turning machine stiffness response and material characteristics by fuzzy rule based pattern matching of cutting force plots

In micro-nano systems technology (MNST), application of mechanical based machining operations such as micro turning, micro milling, micro EDM have shown promising trends to produce micro parts in batch scale. In order to ensure reproducibility better understanding on micro cutting process dynamics and sensitivity of machine stiffness and material characteristics becomes critical. In this paper, a methodology has been developed to assess machine stiffness and material dependent characteristics and demonstrated for micro turning operations conducted on DT-110 micro machining center. In this method, authors incorporate pattern matching algorithm to compare run data image of cutting force plots with that of reference plot. The reference plots of cutting forces v/s time were drawn from simulation run data computed from the micro turning process models. The run data plots of cutting force v/s time were drawn from the processed signal data obtained from the dynamometer during machining operation. The plots were fragmented into patterns and Euclidean distance computed between pair patterns of reference and measured cutting forces v/s time plot image represents the changes happened in machining conditions. This has been used to perform backward calculation to assess the machine stiffness response and material characteristic constants variations over machining time. In order to perform these comparative pattern error adjustments between reference and measured cutting force plots a fuzzy rule based algorithm has been developed.     

制袋分切微机控制系统设计

系统用于制袋分切的纠偏控制,主受包含光电探头、微机处理和电气控制三部分。光电探头采用激光二极管作为光源,光学系统完成标志的成像,双光敏电阻检测标志线的偏移,A／D转换器完成偏移信号的模数转换,数据由微机进行模糊处理。微机处理部分讨论了软硬件的实现情况,并给出了程序流程图。采用固态继电器完成高低压的隔离,纠偏驱动通过变频电机吏现模糊控制。死区的设置大幅减少了噪音,同时大幅提高效率并节约能源。     

A Novel Fuzzy System With Dynamic Rule Base

A new fuzzy system containing a dynamic rule base is proposed in this paper. The novelty of the proposed system is in the dynamic nature of its rule base which has a fixed number of rules and allows the fuzzy sets to dynamically change or move with the inputs. The number of the rules in the proposed system can be small, and chosen by the designer. The focus of this article is mainly on the approximation capability of this fuzzy system. The proposed system is capable of approximating any continuous function on an arbitrarily large compact domain. Moreover, it can even approximate any uniformly continuous function on infinite domains. This paper addresses existence conditions, and as well provides constructive sufficient conditions so that the new fuzzy system can approximate any continuous function with bounded partial derivatives. Finally, an example is given to show how the proposed fuzzy system can be effectively used for system modeling and control     

Evolutionary design of Sugeno-type fuzzy systems for modelling humanoid robots

An evolutionary design of Sugeno-type fuzzy systems for modelling humanoid robots is presented in this article, and issues related to the determination of the antecedent and consequent structures of the fuzzy model are addressed. In the design of the fuzzy model, determination of the type, the number of membership functions assigned to the input variables, the types of consequent equations for the fuzzy rules, the optimal number of input variables, and the dominant input variables among the input candidates are carried out using evolutionary algorithms. Using these algorithms, proper structures are evolved for the antecedent and the consequent of the Sugeno-type fuzzy model. Simulations are performed to show the effectiveness of the developed method when applied to a humanoid robot system with strong nonlinearities that have 10 input candidates.     

投资项目模糊多属性经济评价研究

传统项目经济评价方法在模糊信息的处理过程中,往往倾向于模糊信息清晰化、实数定量化处理,从而使信息失真甚至丢失。为此,研究将模糊数理论引入投资项目经济评价。建立反映项目经济、社会、生态综合效益的多属性经济评价指标体系,利用区间模糊数理论,将定性指标模糊化处理,将定量指标标准化处理,接着通过应用可能度分析方法,研究了基于模糊多属性决策理论的投资项目经济评价方法与步骤,并通过一个实例说明对传统经济评价方法的探索和改进。     

Nature-inspired framework for measuring visual image resemblance: A near rough set approach

The problem considered in this paper is how to determine the degree of nearness between complex visual objects. The proposed solution to this problem stems from a natural computing approach to solving the visual acuity problem in terms of a granular representation of visual information that is quantifiable as well as understandable for humans. This is accomplished via a near rough set framework in the approximation of a pair of disjoint sets and measurement of distances between sets using various fuzzy pseudometrics. Pseudometrics, in general, and fuzzy pseudometrics, in particular, are useful in measuring the distance between pairs of objects such as sets. Such distances are indicators of the nearness of (resemblance between) visual objects. These observations lead to a number of practical applications such as object recognition and object retrieval in digital image analysis. One such application is reported in this article. The contribution of this article is threefold: introduction of a nature-inspired framework for measurement of visual object resemblance, four different incarnations of the standard fuzzy metric and application of fuzzy metrics in content-based image retrieval experiments.     

Non-convex fuzzy data and fuzzy statistics: a first descriptive approach to data analysis

LR-fuzzy numbers are widely used in Fuzzy Set Theory applications based on the standard definition of convex fuzzy sets. However, in some empirical contexts such as, for example, human decision making and ratings, convex representations might not be capable to capture more complex structures in the data. Moreover, non-convexity seems to arise as a natural property in many applications based on fuzzy systems (e.g., fuzzy scales of measurement). In these contexts, the usage of standard fuzzy statistical techniques could be questionable. A possible way out consists in adopting ad-hoc data manipulation procedures to transform non-convex data into standard convex representations. However, these procedures can artificially mask relevant information carried out by the non-convexity property. To overcome this problem, in this article we introduce a novel computational definition of non-convex fuzzy number which extends the traditional definition of LR-fuzzy number. Moreover, we also present a new fuzzy regression model for crisp input/non-convex fuzzy output data based on the fuzzy least squares approach. In order to better highlight some important characteristics of the model, we applied the fuzzy regression model to some datasets characterized by convex as well as non-convex features. Finally, some critical points are outlined in the final section of the article together with suggestions about future extensions of this work.     

A review of the construction of hierarchical fuzzy systems

Fuzzy rule‐based systems are nowadays one of the most successful applications of fuzzy sets and fuzzy logic. Most applications use a flat set of fuzzy rules. However, in complex applications with a large set of variables, it is not appropriate to define the system with a flat set of rules because, among other problems, the number of rules increases exponentially with the number of variables. Hierarchical fuzzy systems are one of the alternatives presented in the literature to overcome this problem. In this article we review the latest results related with this type of fuzzy system. © 2002 Wiley Periodicals, Inc.     

A multi-objective genetic algorithm with fuzzy c-means for automatic data clustering

This article presents a multi-objective genetic algorithm which considers the problem of data clustering. A given dataset is automatically assigned into a number of groups in appropriate fuzzy partitions through the fuzzy c-means method. This work has tried to exploit the advantage of fuzzy properties which provide capability to handle overlapping clusters. However, most fuzzy methods are based on compactness and/or separation measures which use only centroid information. The calculation from centroid information only may not be sufficient to differentiate the geometric structures of clusters. The overlap-separation measure using an aggregation operation of fuzzy membership degrees is better equipped to handle this drawback. For another key consideration, we need a mechanism to identify appropriate fuzzy clusters without prior knowledge on the number of clusters. From this requirement, an optimization with single criterion may not be feasible for different cluster shapes. A multi-objective genetic algorithm is therefore appropriate to search for fuzzy partitions in this situation. Apart from the overlap-separation measure, the well-known fuzzy J_m index is also optimized through genetic operations. The algorithm simultaneously optimizes the two criteria to search for optimal clustering solutions. A string of real-coded values is encoded to represent cluster centers. A number of strings with different lengths varied over a range correspond to variable numbers of clusters. These real-coded values are optimized and the Pareto solutions corresponding to a tradeoff between the two objectives are finally produced. As shown in the experiments, the approach provides promising solutions in well-separated, hyperspherical and overlapping clusters from synthetic and real-life data sets. This is demonstrated by the comparison with existing single-objective and multi-objective clustering techniques.