FUAT – A fuzzy clustering analysis tool

As it is known, fuzzy clustering is a kind of soft clustering method and primarily based on idea of segmenting data by using membership degrees of cases which are computed for each cluster. However, most of the current fuzzy clustering modules packaged in both open source and commercial products have lack of enabling users to explore fuzzy clusters deeply and visually in terms of investigation of different relations among clusters. Furthermore, without a decision maker or an expert, it is hard to decide the number of clusters in fuzzy clustering studies. Therefore, in this study, a desktop software, namely FUAT, is developed to analyze, explore and visualize different aspects of obtained fuzzy clusters which are segmented by fuzzy c-means algorithm. Moreover, to obtain and inform possible natural cluster number, FUAT is equipped with Expectation Maximization algorithm.     

A practical fuzzy controllers scheme of overhead crane

1IntroductionThe overhead crane systemis widely usedinindustryformoving heavy cargos .Thus anti_sway and position controlhave become the requirements as a core technology forautomated crane systemthat are capable of flexible spatialautomatic conveyance .The purpose of crane control is to reduce the swing ofthe load while movingthe trolleyto the desired position asfast as possible .However ,the overhead crane has seriousproblems :the crane acceleration,required for motion,always induces undesir…     

A fuzzy logic system based on Schweizer-Sklar t-norm

In recent years, the basic research of fuzzy logic and fuzzy reasoning is growing ac- tively day by day, such as the basic logic system BL proposed by Hajek[1]; fuzzy logic system MTL proposed by Esteva and Godo[2]; fuzzy reasoning, implication operators …     

A new metric for L–R fuzzy numbers and its application in fuzzy linear systems

In this paper, a metric based on modified Euclidean metric on interval numbers, for L–R fuzzy numbers with fixed $L(\cdot)$ and $R(\cdot)$ is introduced. Then, it is applied for solving L–R fuzzy linear system (L–R-FLS) with fuzzy right-hand side, so that L–R-FLS is transformed to the minimization problem. The solution of the mentioned non-linear programming problem is our favorite fuzzy number vector solution. Two constructive Algorithms are proposed in detail and the method is illustrated by solving several numerical examples.     

A novel fuzzy neural network and its approximation capability

The polygonal fuzzy numbers are employed to define a new fuzzy arithmetic. A novel ex-tension principle is also introduced for the increasing function σ:R→R. Thus it is convenient to con-struct a fuzzy neural network model with succinct learning algorithms. Such a system possesses some universal approximation capabilities, that is, the corresponding three layer feedforward fuzzy neural networks can be universal approximators to the continuously increasing fuzzy functions.     

A fuzzy Lyapunov function approach to estimating the domain of attraction for continuous-time Takagi–Sugeno fuzzy systems

This paper deals with stability analysis and control design problems for continuous-time Takagi–Sugeno (T–S) fuzzy systems. The first aim is to present less conservative linear matrix inequality (LMI) conditions to design controllers and assess the stability. The second relevant contribution is to present a new strategy to find an inner estimate of the domain of attraction (DA) via LMIs. The results are based on the fuzzy Lyapunov functions (FLFs) and non-parallel distributed compensation (non-PDC) approaches. Finally, examples illustrate the effectiveness and merits of the proposed methods.     

A hierarchical fuzzy system for modeling driver’s behavior

The study of human behavior during driving is of primary importance for improving the driver??s security. In this study, we propose a hierarchical driver_vehicle_environment fuzzy system to analyze driver??s behavior under stress conditions on a road. We include climate, road and car conditions in fuzzy modeling. For obtaining fuzzy rules, experts?? opinions are benefited by means of questionnaires on effects of parameters such as climate, road and car conditions on driving capabilities. The number of fuzzy rules is optimized by Particle Swarm Optimization (PSO) algorithm. Also the frequency of pressing on brake and gas pedals and the number of car??s direction changes are used to determine the driver??s behavior under different conditions. Three different positions are considered for driving and decision making; one position in driving lane and two positions in opposite lane. A fuzzy model called Model 1 is presented for modeling the change of steering angle and speed control by considering time distances with existing cars in these three positions, the information about the speed and direction of car, and the steering angle of car. The behaviors of different drivers under two stress conditions are investigated. Also we obtained two other models based on fuzzy rules called Model 2 and Model 3 by using Sugeno fuzzy inference. Model 2 has two linguistic terms and Model 3 has four linguistic terms for estimating the time distances with other cars. The results of three models are compared. The comparative studies have shown that simulation results are in good agreement with the real world situations.     

A simple algorithm for training fuzzy systems using input–output data

This paper proposes a simple algorithm for training fuzzy systems from numerical data. The main advantage of the method is the lack of complicated iterative mechanisms and therefore, its implementation is carried out easily. The suggested algorithm employs a fuzzy model with simplified rules, assuming a fuzzy partition of the input space into fuzzy subspaces. The output is inferred by expanding the model into fuzzy basis functions (FBFs), where each FBF corresponds to a certain fuzzy subspace. The number of rules and the respective premise parts (fuzzy subspaces) are determined using the nearest neighbor approach. Then, the optimal consequent parameters are obtained by the least-squares method. Finally, simulations show the validity of the method.     

Dynamic bandwidth allocation for 3G wireless systems—A fuzzy approach

3G Wireless systems are to support multiple classes of traffic with widely different characteristics and quality of service (QoS) requirements. A major challenge in this system is to guarantee the promised QoS for the admitted users, while maximizing the resource allocation through dynamic resource sharing. In the case of multimedia call, each of the services has its own distinct QoS requirements concerning probability of blocking (P_B), service access delay (SAD), and access delay variation (ADV). The 3G wireless system attempts to deliver the required QoS by allocating appropriate resources (e.g. bandwidth, buffers), and bandwidth allocation is a key in achieving this. Dynamic bandwidth allocation policies reported so far in the literature deal with audio source only. They do not consider QoS requirements. In this work, a fuzzy logic (FL)-based dynamic bandwidth allocation algorithm for multimedia services with multiple QoS (P_B, SAD, ADV, and the arrival rate) requirements are presented and analyzed. Here, each service can declare a range of acceptable QoS levels (e.g. high, medium, and low). As QoS demand varies, the proposed algorithm allocates the best possible bandwidth to each of the services. This maximizes the utilization and fair distribution of resources. The proposed allocation method is validated in a variety of scenarios. The results show that the required QoS can be obtained by appropriately tuning the fuzzy logic controller (FLC).     

A self-organization mining based hybrid evolution learning for?TSK-type fuzzy model design

In this paper, a self-organization mining based hybrid evolution (SOME) learning algorithm for designing a TSK-type fuzzy model (TFM) is proposed. In the proposed SOME, group-based symbiotic evolution (GSE) is adopted in which each group in the GSE represents a collection of only one fuzzy rule. The proposed SOME consists of structure learning and parameter learning. In structure learning, the proposed SOME uses a two-step self-organization algorithm to decide the suitable number of rules in a TFM. In parameter learning, the proposed SOME uses the data mining based selection strategy and data mining based crossover strategy to decide groups and parental groups by the data mining algorithm that called frequent pattern growth. Illustrative examples were conducted to verify the performance and applicability of the proposed SOME method.     

A framework for development of fuzzy GOMS model for human‐computer interaction

The objective of this study was to examine usefulness of fuzzy methodologies in the analysis and design of human‐computer interaction. A framework for generalization of the Goals‐Operators‐Methods‐Selection Rules (GOMS) model, and its fuzzy version was proposed. An experimental verification of the fuzzy GOMS model was also provided. A total of six subjects participated in two laboratory experiments. These experiments were performed in order to validate the proposed fuzzy GOMS model for the text editing task described in information processing terms. The subjects were not familiar with the text files to be edited, and the task was performed from the subject's own office and desk. All subjects were familiar with and regularly used the VI screen editor. The experiments consisted of the following steps: (1) the subject performed a familiar text editing task using a screen editor (VI); (2) the methods by which the subject achieved his goals (word location) as well as selection rules were elicited; (3) several compatibility functions for fuzzy terms used by the subject were derived; and (4) once all the rules, methods, and corresponding membership functions have been elicited, the theory of possibility was used to model the expert's rule selection process. For this purpose, each of the potential rules was assigned a possibility measure equal to the membership value(s) derived during the elicitation phase of experiment Finally, the selected methods were compared to non‐fuzzy predictions and actual experimental data. It was shown that overall, across all subjects and trials of the main editing task, the fuzzy‐based COMS model predicted significantly more of the subject responses, than did the non‐fuzzy COMS model.     

A new approach for ranking of L–R type generalized fuzzy numbers

Ranking of fuzzy numbers play an important role in decision making, optimization, forecasting etc. Fuzzy numbers must be ranked before an action is taken by a decision maker. Cheng (Cheng, C. H. (1998). A new approach for ranking fuzzy numbers by distance method. Fuzzy Sets and Systems, 95, 307–317) pointed out that the proof of the statement “Ranking of generalized fuzzy numbers does not depend upon the height of fuzzy numbers” stated by Liou and Wang (Liou, T. S., & Wang, M. J. (1992). Ranking fuzzy numbers with integral value. Fuzzy Sets and Systems, 50, 247–255) is incorrect. In this paper, by giving an alternative proof it is proved that the above statement is correct. Also with the help of several counter examples it is proved that ranking method proposed by Chen and Chen (Chen, S. M., & Chen, J. H. (2009). Fuzzy risk analysis based on ranking generalized fuzzy numbers with different heights and different spreads. Expert Systems with Applications, 36, 6833–6842) is incorrect. The main aim of this paper is to modify the Liou and Wang approach for the ranking of L–R type generalized fuzzy numbers. The main advantage of the proposed approach is that the proposed approach provide the correct ordering of generalized and normal fuzzy numbers and also the proposed approach is very simple and easy to apply in the real life problems. It is shown that proposed ranking function satisfy all the reasonable properties of fuzzy quantities proposed by Wang and Kerre (Wang, X., & Kerre, E. E. (2001). Reasonable properties for the ordering of fuzzy quantities (I). Fuzzy Sets and Systems, 118, 375–385).     

一种PID模糊控制器(fuzzy PI+fuzzy ID型)

提出一种二维PID模糊控制器,其结构形式筒称为fuzzy PI+fuzzy ID型.根据fuzzy PI和fuzzy ID控制器的图解说明,确定该fuzzy PI和fuzzy ID控制器的模糊控制规则的相似性.理论分析表明,该PID模糊控制器除具有常规PID性能外,还具有非线性等特点.仿真结果表明,与常规PID和fuzzy PI控制相比性能更优.     

A parametric model for cell formation and exceptional elements’ problems with fuzzy parameters

This paper introduces a new fuzzy mathematical model based on the fuzzy parametric programming (FPP) approach for the cellular manufacturing system (CMS) design. The aim of the proposed model is to handle two important problems of CMS design called cell formation (CF) and exceptional elements (EE) simultaneously in fuzzy environment. The model is capable to express vagueness of all the system parameters and gives the decision-maker (DM) alternative decision plans for different grades of precision. So, it is expected to provide a more realistic CMS design for real life problems. To illustrate the model proposed here, an example with fuzzy extension in data set is adopted from literature and computational results are presented.This paper was presented in the 2nd Group Technology/Cellular Manufacturing-World Symposium, Ohio University, Ohio, USA, July 28–30.     

Probability representations of fuzzy systems

1 Background of birth of fuzzy systems As is well-known, just considering the great uncertainties of many systems, Zadeh put forward the notion of fuzzy sets and advanced the idea of the fuzzy reasoning by means of fuzzy sets which could describe a system approximately[1]. Those systems that are constructed on the basis of fuzzy reasoning are called fuzzy systems in general[2―4]. The research on fuzzy systems has attracted broad attention[5―7]. For instance, universal ap- proximation proper…     

A hybrid genetic algorithm–adaptive network-based fuzzy inference system in prediction of wave parameters

An important issue in application of fuzzy inference systems (FISs) to a class of system identification problems such as prediction of wave parameters is to extract the structure and type of fuzzy if–then rules from an available input–output data set. In this paper, a hybrid genetic algorithm–adaptive network-based FIS (GA–ANFIS) model has been developed in which both clustering and rule base parameters are simultaneously optimized using GAs and artificial neural nets (ANNs). The parameters of a subtractive clustering method, by which the number and structure of fuzzy rules are controlled, are optimized by GAs within which ANFIS is called for tuning the parameters of rule base generated by GAs. The model has been applied in the prediction of wave parameters, i.e. wave significant height and peak spectral period, in a duration-limited condition in Lake Michigan. The data set of year 2001 has been used as training set and that of year 2004 as testing data. The results obtained by the proposed model are presented and analyzed. Results indicate that GA–ANFIS model is superior to ANFIS and Shore Protection Manual (SPM) methods in terms of their prediction accuracy.     

A type-2 fuzzy logic controller design for buck and boost DC–DC converters

Conventional (type-1) fuzzy logic controllers have been commonly used in various power converter applications. Generally, in these controllers, the experience and knowledge of human experts are needed to decide parameters associated with the rule base and membership functions. The rule base and the membership function parameters may often mean different things to different experts. This may cause rule uncertainty problems. Consequently, the performance of the controlled system, which is controlled with type-1 fuzzy logic controller, is undesirably affected. In this study, a type-2 fuzzy logic controller is proposed for the control of buck and boost DC–DC converters. To examine and analysis the effects of the proposed controller on the system performance, both converters are also controlled using the PI controller and conventional fuzzy logic controller. The settling time, the overshoot, the steady state error and the transient response of the converters under the load and input voltage changes are used as the performance criteria for the evaluation of the controller performance. Simulation results show that buck and boost converters controlled by type-2 fuzzy logic controller have better performance than the buck and boost converters controlled by type-1 fuzzy logic controller and PI controller.     

A fuzzy PLC with gain-scheduling control resolution for a thermal process – a case study

This paper presents a case study on the practical implementation of a fuzzy-PLC system for a thermal process. The theoretical study indicates that the inferior performance of fuzzy-controlled processes around a reference point is often caused by insufficient resolution of the fuzzy inference. The limitations of ladder logic cannot support complex algorithms for resolution improvement. A simple gain adaptation method is presented here, to achieve smooth fuzzy control, that can be easily implemented in a PLC system. Real-time experiments on an unidentified thermal process show the effectiveness of the approach, as well as the robustness of the fuzzy controller with respect to the time-varying features of the process.