Fuzzy mathematical approach to pattern recognition
Fuzzy mathematical approach to pattern recognition
Fuzzy mathematical techniques with applications
Fuzzy mathematical techniques with applications
Fuzzy sets, decision making and expert systems
Fuzzy sets, decision making and expert systems
Fuzzy sets and applications
Hedge algebras: an algebraic approach to structure of sets of linguistic truth values
Fuzzy Sets and Systems
Fuzzy expert systems
Extended hedge algebras and their application to fuzzy logic
Fuzzy Sets and Systems
IEEE Spectrum
Essentials of fuzzy modeling and control
Essentials of fuzzy modeling and control
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy Control Systems
Fuzzy Control and Fuzzy Systems
Fuzzy Control and Fuzzy Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
Design and implementation of the tree-based fuzzy logic controller
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Up-to-Date Bibliography of Current-Mode Design
Analog Integrated Circuits and Signal Processing
A Study on the Evolutionary Adaptive Defuzzification Methods in Fuzzy Modeling
International Journal of Hybrid Intelligent Systems
Development of an adaptive neuro-fuzzy classifier using linguistic hedges: Part 1
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
In this paper, a current-mode design methodology to implement a set of fuzzy linguistic hedge circuits is proposed. The so-called fuzzy linguistic hedge is a fuzzy operation applied to adjust the membership function of a fuzzy set. The fuzzy membership function of control variable and the control rules are very important in a fuzzy logic controller because they dominate the control strategies. If the control results fail to meet the system requirements, the control objective can still be achieved by adjusting the membership function of the fuzzy set or the control rules. Moreover, the adjustment effect of the control strategies through the modifications of the fuzzy membership function is the same as that of the system control rules. In this paper, we propose a set of fuzzy linguistic hedge circuits, including absolutely, very, much more, more, plus minus, more or less, slightly, and contrast intensification, which has been fabricated in 0.8 μm CMOS process. Experimental results show that the average error of the circuits is within 1% of the full scale current. Under the power supply voltage of 3.3 V, the operating dynamic range is 50 μA. Furthermore, these circuits still work well even when the power supply voltage is down to 2.5 V. In addition, in real world application, we can incorporate a membership function generator, a fuzzification unit, a multi-input maximum/minimum circuit, and a defuzzification unit with the linguistic hedge used to modify the membership function in order to develop a real-time adaptive fuzzy logic controller.