FUZZY SET THEORY Classical Set Theory Fuzzy Set Theory Interval Arithmetic Operations on Fuzzy Sets FUZZY LOGIC THEORY Classical Logic Theory The Boolean Algebra Multi-Valued Logic Fuzzy Logic and Approximate Reasoning Fuzzy Relations Fuzzy Logic Rule Base FUZZY SYSTEM MODELING Modeling of the Static Fuzzy Systems Stability Analysis of Discrete-Time Dynamic Fuzzy Systems Modeling of Continuous-Time Dynamic Fuzzy Systems Stability Analysis of Continuous-Time Fuzzy Systems Controllability Analysis of Continuous-Time Dynamic Fuzzy Systems Analysis of Nonlinear Continuous-Time Dynamic Fuzzy Systems FUZZY CONTROL SYSTEMS Classical Programmable Logic Control Fuzzy Logic Control I: A General Model-Free Approach Fuzzy Logic Control II: A General Model-Based Approach FUZZY PID CONTROLLERS Conventional PID Controllers Fuzzy PID Controllers Fuzzy PID Controllers: Stability Analysis ADAPTIVE FUZZY CONTROL Fundamental Adaptive Fuzzy Control Concept Gain Scheduling Fuzzy Self-Tuning Regulator Model Reference Adaptive Fuzzy Systems Dual Control Sub-Optimal Fuzzy Control APPLICATIONS IN FUZZY CONTROL Health Monitoring Fuzzy Diagnostic Systems Fuzzy Control of Image sharpness for Auto-focus Cameras Fuzzy Control for Servo Mechanic Systems Fuzzy PID Controllers for Servo Mechanic Systems Fuzzy Controllers for Robotic Manipulator Note: Each chapter also contains Problems and References

Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems

Selecting the most efficient maintenance approach using fuzzy multiple criteria decision making

Measurement of ground contact forces (GCFs) provides necessary information to detect human gait phases. In this paper, a new analysis method of the GCF signals is discussed for detection of the gait phases. Human gaits are complicated, and the gait phases cannot be exactly distinguished by comparing sensor outputs to a threshold. This paper proposes a method by fuzzy logic for detecting the gait phases continuously and smoothly. The smooth and continuous detection of the gait phases enables a full use of information obtained from GCF sensors. For advanced rehabilitation systems, this paper also introduces a higher level algorithm that quantitatively monitors the amount of abnormalities in a human gait. The abnormalities detected by the proposed method include an improper GCF pattern as well as an incorrect sequence of the gait phases. To realize the monitoring algorithm, the gait phases are analyzed as a vector and the abnormalities are detected by simple kinematic equations. The proposed methods are implemented by using signals from sensor-embedded shoes called smart shoes. Each smart shoe has four GCF sensors installed between the cushion pad and the sole. The GCF sensor applies an air pressure sensor connected to an air bladder. A gait monitoring system that integrates the proposed methods is shown in this paper and verified for both normal and abnormal gaits.

A Gait Monitoring System Based on Air Pressure Sensors Embedded in a Shoe

The classification of very high resolution remote sensing images from urban areas is addressed by considering the fusion of multiple classifiers which provide redundant or complementary results. The proposed fusion approach is in two steps. In a first step, data are processed by each classifier separately, and the algorithms provide for each pixel membership degrees for the considered classes. Then, in a second step, a fuzzy decision rule is used to aggregate the results provided by the algorithms according to the classifiers' capabilities. In this paper, a general framework for combining information from several individual classifiers in multiclass classification is proposed. It is based on the definition of two measures of accuracy. The first one is a pointwise measure which estimates for each pixel the reliability of the information provided by each classifier. By modeling the output of a classifier as a fuzzy set, this pointwise reliability is defined as the degree of uncertainty of the fuzzy set. The second measure estimates the global accuracy of each classifier. It is defined a priori by the user. Finally, the results are aggregated with an adaptive fuzzy operator ruled by these two accuracy measures. The method is tested and validated with two classifiers on IKONOS images from urban areas. The proposed method improves the classification results when compared with the separate use of the different classifiers. The approach is also compared with several other fuzzy fusion schemes

/pdf/decision-fusion-for-the-classification-of-urban-remote-25twz6u57k.pdf

Decision Fusion for the Classification of Urban Remote Sensing Images

Modelling risk allocation decision in construction contracts

Fuzzy logic has become an important tool for a number of different applications ranging from the control of engineering systems to artificial intelligence. In this concise introduction, the author presents a succinct guide to the basic ideas of fuzzy logic, fuzzy sets, fuzzy relations, and fuzzy reasoning, and shows how they may be applied. The book culminates in a chapter which describes fuzzy logic control: the design of intelligent control systems using fuzzy if-then rules which make use of human knowledge and experience to behave in a manner similar to a human controller. Throughout, the level of mathematical knowledge required is kept basic and the concepts are illustrated with numerous diagrams to aid in comprehension. As a result, all those curious to know more about fuzzy concepts and their real-world application will find this a good place to start.

一男田中

Papers

An Introduction to Fuzzy Logic for Practical Applications

一男 田中

Papers

An Introduction to Fuzzy Logic for Practical Applications

一男田中