基于模糊逻辑的机器人导航系统研究

由于未知环境下机器人导航容易出现死锁问题,设计了一种基于栅格的地图模型叫“数据栅格”,并在此基础上提出了一种基于行为的导航方法即“安全导航法”。数据栅格记录了周围环境中障碍物信息和机器人路径信息,安全导航法就是应用数据栅格技术来解决未知环境下机器人导航遇到的死锁问题。模糊逻辑用来设计和协调各种导航行为。仿真和实际环境的实验结果也证实了该方法的良好性能。     

Fuzzy logic-based embedded system for video de-interlacing

Video de-interlacing algorithms perform a crucial task in video processing. Despite these algorithms are developed using software implementations, their implementations in hardware are required to achieve real-time operation. This paper describes the development of an embedded system for video de-interlacing. The algorithm for video de-interlacing uses three fuzzy logic-based systems to tackle three relevant features in video sequences: motion, edges, and picture repetition. The proposed strategy implements the algorithm as a hardware IP core on a FPGA-based embedded system. The paper details the proposed architecture and the design methodology to develop it. The resulting embedded system is verified on a FPGA development board and it is able to de-interlace in real-time.     

Fuzzy logic-based induction motor protection system

The protection is very important to detect abnormal motor running conditions such as over current, over voltage, overload, over temperature, and so on. When a failure is sensed by the protection system, a time delay should be specified to trip the motor. In the classical systems, motors are stopped with the time delay, which is adjusted constantly without considering the fault level. This paper presents a fuzzy logic-based protection system covering six different fault parameters for induction motors. This paper focuses on a new time-delay calculation for stopping induction motor and improves the overall detection performance. The time delay is computed by fuzzy logic method according to various fault parameters when one of the failures occurs on the motor. This system is successfully tested in real-time faults on the motor, and it shows that it provides sensitive protection by fuzzy rules.     

Fuzzy logic-based decision support system for automating ergonomics risk assessments

Workers in the modular construction industry are frequently exposed to ergonomic risks, which may lead to injuries and lower productivity. In light of this, researchers have proposed a number of ergonomics risk assessment methods to identify design flaws in work systems, thereby reducing ergonomic discomfort and boosting workplace productivity. However, organizations often disregard ergonomics risk assessments due to a lack of convenient tools and knowledge. Therefore, this study proposes a fuzzy logic-based decision support system to help practitioners to automatically and comprehensively assess the ergonomic performance of work systems. For comprehensive assessment of ergonomic risk, the proposed decision support system considers physical, environmental, and sensory factors. Specifically, the decision support system comprises eight fuzzy expert systems that output a composite risk score, called an “ergonomic risk indicator”, that indicates the overall level of ergonomic risk present in a given work system. The performance of the proposed decision support system is then evaluated using a real-world case study in a modular construction facility by comparing the results of the decision support system with the facility's occupational injury reports. The results prove the effectiveness of the decision support system. Overall, the decision support system is capable of generating a composite risk score, the ergonomic risk indicator, and the proposed high-level architecture and design represent significant contributions for the enhancement of health and safety in the modular construction industry.     

Fuzzy logic-based optimization for redundant manipulators

Redundant manipulators have more degrees of freedom (DOF) than the DOF of the task space. This implies that the number of joint position variables is greater than the number of variables specifying the task. The problem of solving the kinematic equations for the joint variables is underspecified unless additional equations/constraints are introduced to obtain a well-posed problem. A dynamic level redundancy resolution is proposed. The joint space model is transformed to a reduced-order model in the pseudovelocity space. The elements of the foregoing transformation matrix indirectly determine the contribution of each joint to the total motion. These elements are selected using two fuzzy logic-based methods so as to minimize the instantaneous manipulator power: (1) in the velocity method, a space vector in the velocity relationship between the two spaces is determined by imposing a constraint on the continuity of the joint velocities at the time instant when the elements of the transformation matrix experience a discontinuity and (2) in the torque method, an alternative approach introduced to reduce the computational complexity, the changes in the transformation matrix are made continuous with respect to time by the appropriate choice of a space vector in the joint torque expression. Simulations are given.     

Fuzzy logic-based forecasting model

In this paper a fuzzy logic-based software tool, fuzzy logic advisory tool (FLAT), for demand forecasting of signal transmission products is presented. The FLAT was developed for the prediction of demand of about 1000 different products in order to aid materials purchasing process of about 14,000 different components in the electronics manufacturing processes of Nokia Network Systems's Haukipudas factory. The prediction values of different products are inferred by starting from a set of eight input values. Each input value is fuzzied by the FLAT. Thereafter, fuzzy results are inferred in three sequential phases. In each phase the number of variables is split due to hierarchical structure of the inference module. A data base and a rule base are divided accordingly into three hierarchical levels. Rules are represented by linguistic relations changed into matrix equations form in order to apply linguistic equations framework technique (LE). Fuzzy membership functions for input values are determined on-line from earlier input values of the products. Fuzzy rules were inferred by analyzing behavior of the products together with market experts and product experts of the company. The model is able to produce more accurate decision-making support than more traditional approaches. This is probably due to the model-based approach and systematic data management.     

Fuzzy rule classifier: Capability for generalization in wood color recognition

In this paper, a classification method based on fuzzy linguistic rules is exposed. It is applied for the recognition of the gradual color of wood in an industrial context. The wood, which is a natural material, implies uncertainty in the definition of its color. Moreover, the timber context leads obtaining imprecise data. Several factors can have an impact on the sensors (ageing of the acquisition system, variation of the ambient temperature, etc.). Finally, the data sets are often small and incomplete. Thus the proposed method must work within these constraints, and must be compatible with the time-constraint of the system. This generally imposes a weak complexity of the recognition system. The Fuzzy Rule Classifier is split in two main parts, the fuzzification step and the rule generation step. To improve the tuning of this classifier, a specific fuzzification method is presented and compared with more classical ones. Several comparisons have been made with other classification method such as neural network or support vector machine. This experimental study showed the suitability of the proposed approach essentially in term of generalization capabilities from small data sets, and recognition rate improvement.     

Fuzzy logic-based learning system and estimation of state-of-charge of lead-acid battery

The objective of this work is to develop a state-of-charge (SOC) estimation system for the lead-acid battery, which is free from the time-dependent variation of the battery characteristics. In this system, the SOC is estimated by an improved Coulomb metric method, and the time-dependent variation is compensated by using a learning system. The learning system tunes the Coulomb metric method in such a way that the estimation process remains error free from the time-dependent variation. The proposed learning system uses the fuzzy logic, which is not used for estimation of SOC but perform as a component of learning system. The fuzzy logic is used as a soft computing device for a multi-variables function evolution. During learning process the system automatically generates a new fuzzy rule base, and replaces the old fuzzy rule base. Results of the simulations as well as the experiments on an 8-bit microcontroller are also included which indicate the effectiveness of the proposed method.     

基于模糊逻辑推理的WSNs非均匀分簇算法

为了进一步降低无线传感网络WSNs(Wireless Sensor Networks)能耗,拓延网络寿命,提出了基于模糊逻辑推理的WSNs非均匀分簇算法,记为DUCF.DUCF算法充分考虑了节点剩余能量、节点度以及离基站距离.根据经验制定模糊规则,通过模糊推理系统得到节点当选为簇头的几率和簇尺寸.DUCF算法形成非均匀簇,进而平衡簇头间的能量消耗.仿真结果表明,DUCF算法在网络寿命、能量消耗方面的性能优于LEACH、CHEF和EAUCF算法.     

Fuzzy logic-based expert system to predict the results of finite element analysis

In this paper, a fuzzy logic (FL)-based expert system (ES) has been developed to predict the results of finite element (FE) analysis, while solving a rubber cylinder compression problem. As the performance of an ES depends on its knowledge base (KB), an attempt is made to develop the KB through three different approaches by using a genetic algorithm (GA). To collect the training data, two input parameters, namely element size and shape ratio are varied, while solving the said physical problem using an FEM package. The performance of the trained fuzzy logic-based expert system is tested for several test cases, differing significantly from the training cases. Results of these approaches are compared with those of FE analysis. Once developed, the ES is able to determine the values of parameters to be used in FE analysis, in order to obtain the results within a reasonable accuracy, at the cost of a much lower computation compared to that of the FEM package itself.     

A logic-based transformation system

In spite of advances in various transformation systems the transformation of a nonmonotonic-logic-based requirements specification into a procedural (imperative) language program has not been investigated. This paper presents a logic-based transformation system that can transform a nonmonotonic-logic-based specification, the Frame-and-Rule Oriented Requirement Specification Language (FRORL), into procedural language programs. We discuss how to handle nonmonotonic inheritance in FRORL and then establish a matrix-based data flow and dependency analysis mechanism to find all the possible data transformation paths in a logic-based specification. Using a newly developed algorithm, we can adjust the execution sequence of a logic-based specification so that the functions included in the logic-based specification can be represented by a sequential procedural language program     

Fuzzy logic-based decision-making for fault diagnosis in a DC motor

The uncertainty of system models, the presence of noise and the stochastic behaviour of several variables reduce the reliability and robustness of the fault diagnosis methods. To tackle these kinds of problems, this paper presents a decision-making module based on fuzzy logic for model-based fault diagnosis applications. Fuzzy rules use the concept of fault possibility and knowledge of the sensitivities of the residual equations. A fault detection and isolation system, based on the input–output linear model parity equations approach, and including this decision-making module, has been successfully applied in laboratory equipment, resulting in a reduction of the uncertainty due to disturbances and modelling errors. Furthermore, the experimental sensitivity values of the residual equations allow the fault size to be estimated with sufficient accuracy.     

Fuzzy logic-based design of the generalized adaptation algorithm for the discrete control system of flexible spacecraft

Consideration was given to a generalization of an earlier adaptation algorithm developed for the case where two elastic modes make significant contribution to the elastic oscillations arising in the spacecraft structure under the action of discrete control. Sensitivity of the individual modes of elastic oscillations to the adaptation parameter, the discreteness period T ₀ of the control system, was analyzed. In addition to the previous algorithm, designed were relations in other linguistic variables and the control structure carrying out the choice of the working areas of the adapted parameter and its transition from one domain to another with the aim of providing successive damping of both significant modes of elastic oscillations. Results of modeling a flexible spacecraft with adaptive control system were presented.     

Fuzzy logic-based coordinated control method for multi-type battery energy storage systems

In order to take full advantage of the complementary nature of multi-type energy storage and maximally increase the capability of tracking the scheduled wind power output, a charging–discharging control strategy for a battery energy storage system (BESS) comprising many control coefficients is established, and a power distribution method employing fuzzy control principles to optimize the multi-type BESS is proposed, so as to reduce the error of day-ahead short-term wind power prediction. A simulation analysis, taking a typical wind farm output as an actual data sample, showed that the proposed fuzzy logic control method for the multi-type BESS is uniquely flexible and adaptable in achieving the control effect of improving the capability of tracking the scheduled wind power output.     

Fuzzy logic-based matching pursuits for lossless predictive coding of still images

This paper presents an application of fuzzy-logic techniques to the reversible compression of grayscale images. With reference to a spatial differential pulse code modulation (DPCM) scheme, prediction may be accomplished in a space-varying fashion either as adaptive, i.e., with predictors recalculated at each pixel, or as classified, in which image blocks or pixels are labeled in a number of classes, for which fitting predictors are calculated. Here, an original tradeoff is proposed; a space-varying linear-regression prediction is obtained through fuzzy-logic techniques as a problem of matching pursuit, in which a predictor different for every pixel is obtained as an expansion in series of a finite number of prototype nonorthogonal predictors, that are calculated in a fuzzy fashion as well. To enhance entropy coding, the spatial prediction is followed by context-based statistical modeling of prediction errors. A thorough comparison with the most advanced methods in the literature, as well as an investigation of performance trends and computing times to work parameters, highlight the advantages of the proposed fuzzy approach to data compression.     

Fuzzy logic-based prediction of compressive strength of lightweight geopolymers

In the present work, compressive strength of lightweight inorganic polymers (geopolymers) produced by fine fly ash and rice husk–bark ash together with palm oil clinker (POC) aggregates has been investigated experimentally and modeled based on fuzzy logic. To build the model, training, validating and testing were conducted using experimental results from 144 specimens. The used data in the ANFIS models were arranged in a format of six input parameters that cover the quantity of fine POC particles, the quantity of coarse POC particles, the quantity of FA + RHBA mixture, the ratio of alkali activator to ashes mixture, the age of curing and the test trial number. According to these input parameters, in the model, the compressive strength of each specimen was predicted. The training, validating and testing results in the model have shown a strong potential for predicting the compressive strength of the geopolymer specimens in the considered range.     

普适环境中基于模糊理论的信任评估模型

信任管理是当前普适环境中信息安全研究的热点。为了解决主观信任的模糊性和主体之间信任关系的动态性,在模糊数学中的模糊理论的基础上,提出了一个用户信任度的评估模型。该模型是将模糊理论应用到信任关系的不确定性和动态性上,对影响信任主体的主要因素进行综合评估,得出一个总的信任度来确定是否可以信任主体以便进行交互。该模型可以全面地评估用户的信任度,很大程度上保证了普适行为的安全可靠,为处理普适计算的信任管理的不确定研究提供了一个新的方法。