FUZZY CONTROL, FUZZY GRAPHS, AND FUZZY INFERENCE

Fuzzy inference has a multigranular architecture consisting of symbols and continuous values, and has worked well to incorporate experts' know-how into fuzzy controls. Stability analysis of fuzzy control systems is one of the main topics of fuzzy control. A recently proposed stability analysis method on the symbolic level opened the door to the design of stable fuzzy controller using symbols. However the validity of the stability analysis in the symbolic system is not guaranteed in the continuous system. To guarantee this validity, a nonseparate condition has been introduced. If the fuzzy control system is asymptotically stable in the symbolic system and the system satisfies the nonseparate condition, the continuous system is also asymptotically stable. However this condition is too conservative. The new condition called a relaxed nonseparate condition has been proposed and the class of control systems with guaranteed discretization has been expanded. However the relaxed condition has been applicable only to controf systems having symmetric membership functions. This paper presents a new fuzzy inference method that makes the relaxed condition applicable to fuzzy control systems with asymmetric membership functions. Simulations are done to demonstrate the effectiveness of the new fuzzy inference method. The proof of the expansion of the relaxed nonseparate condition is also given.

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Study of Tobacco-Redrying System Based on Fuzzy PID Control

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.220-223.1240 ◽

2012 ◽

Vol 220-223 ◽

pp. 1240-1243

Author(s):

Yu Yu Zhu ◽

Ya Jun Zhou

Keyword(s):

Fuzzy Control ◽

Pid Control ◽

Fuzzy Inference ◽

Fuzzy Pid ◽

Fuzzy Pid Control ◽

Control Scheme ◽

Simulation Results ◽

Performance Target

The process of tobacco-redrying has the characteristics of lagging, uncertainty and being nonlinear, so it is unable to satisfy every performance target only by using the traditional PID controlling method. In response to this reality, this paper, by using the fuzzy inference ability of fuzzy control, proposes a fuzzy PID based control scheme to achieve the online adjustment of the PID parameters in tobacco-redrying process and to make them in the required range. Simulation results show that the controller can effectively control the process of tobacco-redrying.

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Some Recent Defuzzification Methods

Theoretical and Practical Advancements for Fuzzy System Integration - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-1848-8.ch002 ◽

2017 ◽

pp. 31-48 ◽

Cited By ~ 2

Author(s):

Harendra Kumar

Keyword(s):

Fuzzy Logic ◽

Control System ◽

Fuzzy Control ◽

Fuzzy Sets ◽

Fuzzy Set ◽

Fuzzy Number ◽

Fuzzy Inference ◽

Fuzzy Logic System ◽

Single Number ◽

Logic System

Defuzzification is a process that converts a fuzzy set or fuzzy number into a crisp value or number. Defuzzification is used in fuzzy modeling and in fuzzy control system to convert the fuzzy outputs from the systems to crisp values. This process is necessary because all fuzzy sets inferred by fuzzy inference in the fuzzy rules must be aggregated to produce one single number as the output of the fuzzy model.There are numerous techniques for defuzzifying a fuzzy set; some of the more popular techniques are included in fuzzy logic system. In the present chapter some recent defuzzification methods used in the literature are discussed with examples.

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Some Recent Defuzzification Methods

Fuzzy Systems ◽

10.4018/978-1-5225-1908-9.ch044 ◽

2017 ◽

pp. 1003-1019

Author(s):

Harendra Kumar

Keyword(s):

Fuzzy Logic ◽

Control System ◽

Fuzzy Control ◽

Fuzzy Sets ◽

Fuzzy Set ◽

Fuzzy Number ◽

Fuzzy Inference ◽

Fuzzy Logic System ◽

Single Number ◽

Logic System

Defuzzification is a process that converts a fuzzy set or fuzzy number into a crisp value or number. Defuzzification is used in fuzzy modeling and in fuzzy control system to convert the fuzzy outputs from the systems to crisp values. This process is necessary because all fuzzy sets inferred by fuzzy inference in the fuzzy rules must be aggregated to produce one single number as the output of the fuzzy model.There are numerous techniques for defuzzifying a fuzzy set; some of the more popular techniques are included in fuzzy logic system. In the present chapter some recent defuzzification methods used in the literature are discussed with examples.

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Professor Ebrahim Mamdani Memorial Issue

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2012.p0591 ◽

2012 ◽

Vol 16 (5) ◽

pp. 591-591

Author(s):

Yoshinori Arai ◽

Toshihiko Watanabe

Keyword(s):

Fuzzy Logic ◽

Fuzzy Control ◽

Control Systems ◽

Fuzzy Logic Controller ◽

Fuzzy Inference ◽

Special Issue ◽

Fuzzy Algorithms ◽

Dynamic Plant ◽

Memorial Issue ◽

The Many

On February 22, 2010, Prof. Ebrahim H. Mamdani who devised Mamdani fuzzy inference has passed away. His work in fuzzy inference, which rapidly paved the way to its practical use, helped disseminate Prof. Lotfi Zadehfs fuzzy logic and the development of fuzzy research. Prof. Mamdanifs two papers on fuzzy inference ? gApplication of fuzzy algorithms for control of simple dynamic planth (Proc. IEE, Vol.121, No. 12, pp. 1585-1588, 1974) and gAn experiment in linguistic synthesis with a fuzzy logic controllerh (Int. J. of Man-Machine Studies, Vol.7, No.1, pp. 1-13, 1975) with S. Assilian ? enabled fuzzy inference technology to develop dramatically both indicatively and indirectly to where it has been applied, including fuzzy control systems. This special issue honors Prof. Mamdani for his invaluable efforts in these and many other fields. We have asked for submissions by researchers influenced by Prof. Mamdanifs achievements, including his work in fuzzy inference, and have narrowed down to one review and seven full papers. The review by Hirosato Seki and Kai Meng Tay provides an incisive overview on the many aspects of fuzzy inference that Prof. Mamdani brought to light. Prof. Mamdanifs fuzzy inference has become a deterministic technology that can be chosen naturally and that will continue to be influential and survivable well into the future.

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Monitoring Quality of Components by Fuzzy Control Principle

International Journal of Reliability Quality and Safety Engineering ◽

10.1142/s0218539398000145 ◽

1998 ◽

Vol 05 (02) ◽

pp. 133-145

Author(s):

Henry Lau

Keyword(s):

Expert System ◽

Fuzzy Control ◽

Fuzzy Inference ◽

Dimensional Accuracy ◽

Boolean Logic ◽

Fuzzy Expert System ◽

Fuzzy Variables ◽

Plastic Parts ◽

Control Principle

A fuzzy expert system uses fuzzy logic control,1 which is based on a "superset" of Boolean logic that has been extended to handle the concept of "partial truth." It replaces mathematical models with models that are built from a number of rules with fuzzy variables such as output temperature, and fuzzy terms such as extremely hot, fairly cold. A fuzzy expert system has been implemented in a plastic moulding shop in Australia for monitoring dimensional quality of output products. Because these plastic parts are used as assembly components for production of gear boxes, their dimensional accuracy is of utmost importance. This paper presents the implementation of this monitoring system adopting a graphical and non-mathematical approach, and examines the application of fuzzy control systems in quality control. Practical examples with descriptions of how the fuzzy rules are shown and the operations of the fuzzy inference engine are covered.

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Study on Adaptive Fuzzy Control System of Settlement Technology

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1061-1062.904 ◽

2014 ◽

Vol 1061-1062 ◽

pp. 904-907

Author(s):

Xiang Ping Chen

Keyword(s):

Control System ◽

Fuzzy Control ◽

Fuzzy Inference ◽

Control Method ◽

Dynamic Control ◽

Adaptive Fuzzy Control ◽

Fuzzy Control System ◽

Adaptive Fuzzy ◽

The Mathematical Model ◽

Clear Solution

Considering the production status of red mud at present, an adaptive fuzzy control system, according to fuzzy control and genetic algorithm, has been focused on. With the control of flocculants, the system fuzzy control clarity of clear solution. Adaptive neural network fuzzy inference theory is adopted to establish the mathematical model of controlled object "black box", and MATLAB for simulation, showing that the control method has good accuracy and dynamic control quality. Satisfy the requirements of practice work.

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Performance Analysis of Adaptive Neuro Fuzzy Inference System Control for MEMS Navigation System

Mathematical Problems in Engineering ◽

10.1155/2014/961067 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 3

Author(s):

Ling Zhang ◽

Jianye Liu ◽

Jizhou Lai ◽

Zhi Xiong

Keyword(s):

Fuzzy Control ◽

High Precision ◽

Navigation System ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Inertial Sensors ◽

Inertial Sensor ◽

Low Cost ◽

Inference System ◽

Neuro Fuzzy

Characterized by small volume, low cost, and low power, MEMS inertial sensors are widely concerned and applied in navigation research, environmental monitoring, military, and so on. Notably in indoor and pedestrian navigation, its easily portable feature seems particularly indispensable and important. However, MEMS inertial sensor has inborn low precision and is impressionable and sometimes goes against accurate navigation or even becomes seriously unstable when working for a period of time and the initial alignment and calibration are invalid. A thought of adaptive neuro fuzzy inference system (ANFIS) is relied on, and an assistive control modulated method is presented in this paper, which is newly designed to improve the inertial sensor performance by black box control and inference. The repeatability and long-time tendency of the MEMS sensors are tested and analyzed by ALLAN method. The parameters of ANFIS models are trained using reasonable fuzzy control strategy, with high-precision navigation system for reference as well as MEMS sensor property. The MEMS error nonlinearity is measured and modulated through the peculiarity of the fuzzy control convergence, to enhance the MEMS function and the whole MEMS system property. Performance of the proposed model has been experimentally verified using low-cost MEMS inertial sensors, and the MEMS output error is well compensated. The test results indicate that ANFIS system trained by high-precision navigation system can efficiently provide corrections to MEMS output and meet the requirement on navigation performance.

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