The history of fuzzy logic methods

Prediction and interpretation of behavior of complex medical or industrial systems are possible due to application of expert systems. This kind of expert systems emulates an ability to make decisions like a human expert. The emulation built on the fuzzy logic, which is integrated into the system. The author of the theory of the fuzzy logic or fuzzy sets is a professor from the University of California, Berkeley - L. Zadeh. His theory permits the determination of quantitative degree of the belonging of all elements included in a certain set. However, in common theory of sets elements have only to states - an element must belong or must not belong to the pack

Adaptive P Control and Adaptive Fuzzy Logic Controller with Expert System Implementation for Robotic Manipulator Application

This study aims to develop an expert system implementation of P controller and fuzzy logic controller to address issues related to improper control input estimation, which can arise from incorrect gain values or unsuitable rule-based designs. The research focuses on improving the control input adaptation by using an expert system to resolve the adjustment issues of the P controller and fuzzy logic controller. The methodology involves designing an expert system that captures error signals within the system and adjusts the gain to enhance the control input estimation from the main controller. In this study, the P controller and fuzzy logic controller were regulated, and the system was tested using step input signals with small values and larger than the saturation limit defined in the design. The PID controller used CHR tuning to least overshoot, determining the system's gain. The tests were conducted using different step input values and saturation limits, providing a comprehensive analysis of the controller's performance. The results demonstrated that the adaptive fuzzy logic controller performed well in terms of %OS and settling time values in system control, followed by the fuzzy logic controller, adaptive P controller, and P controller. The adaptive P controller showed similar control capabilities during input saturation, as long as it did not exceed 100% of the designed rule base. The study emphasizes the importance of incorporating expert systems into control input estimation in the main controller to enhance the system efficiency compared to the original system, and further improvements can be achieved if the main processing system already possesses adequate control ability. This research contributes to the development of more intelligent control systems by integrating expert systems with P controllers and fuzzy logic controllers, addressing the limitations of traditional control systems and improving their overall performance

Penggunaan Metode Fuzzy Inference System (Fis) Mamdani Dalam Pemilihan Peminatan Mahasiswa Untuk Tugas Akhir

Fuzzy logic is a branch of artificial intelligence to build expert systems. Fuzzy logic is often used as problem solver in a system that does not use numbers, but linguistics or unobvious variables. One of the implementation of fuzzy logic is decision making in determine the topic of final assignment, especially bachelor thesis. IBI Darmajaya has some study programs in the faculty of Computer Science. One of those study programs is Informatics Engineering. The study program of Informatics Engineering leads the students to the specialization of Web Development and Multimedia, Software Engineering, or Expert System for their bachelor thesis. During this time, the specialization that taken by students is not actually what they expert in or the grade they earn from each subject. Usually, students take the specialization because of many students take that field. As the result, they have difficulties to finish their bachelor thesis. This research uses Fuzzy Inference System of Mamdani with 12 input variables, 37 rules, and 3 thesis specializations. This research aims to generate a fuzzy system as a decision maker to determine the thesis specialization

Fuzzy Logic and Neural Networks - a Glimpse of the Future

In 1965 Lofti Zadeh published his paper on fuzzy set theory , putting it forward as a way of more closely realising the human thought process. Many systems developed to aid human activities have been based on definitive , yes/no, type decision making processes. An example is the way all computers are based on the binary logic system where only two possible and separate logic levels are allowed, a logic 1 or logic 0. However, we know from everyday experience that humans think in terms of vague linguistic categories, for example, the weather is fairly good today. Fairly good represents a vague category that can be represented by a fuzzy set which allows values to belong to the set by a varying degree from 0 up to 1. The grade of membership is not a probability , it is a measure of the compatibility of an object with the concept represented by the fuzzy set. Since Zadeh proposed his theory many areas of applications have been considered to assess the suitability of applying fuzzy set theory. Areas include fuzzy logic and approximate reasoning, expert systems, pattern recognition, fuzzy decision making in economics and medicine and fuzzy control. However, it is in the area of fuzzy logic control that most success has been achieved

Improving security requirements adequacy: an interval type 2 fuzzy logic security assessment system

Organizations rely on security experts to improve the security of their systems. These professionals use background knowledge and experience to align known threats and vulnerabilities before selecting mitigation options. The substantial depth of expertise in any one area (e.g., databases, networks, operating systems) precludes the possibility that an expert would have complete knowledge about all threats and vulnerabilities. To begin addressing this problem of fragmented knowledge, we investigate the challenge of developing a security requirements rule base that mimics multi-human expert reasoning to enable new decision-support systems. In this paper, we show how to collect relevant information from cyber security experts to enable the generation of: (1) interval type-2 fuzzy sets that capture intra- and inter-expert uncertainty around vulnerability levels; and (2) fuzzy logic rules driving the decision-making process within the requirements analysis. The proposed method relies on comparative ratings of security requirements in the context of concrete vignettes, providing a novel, interdisciplinary approach to knowledge generation for fuzzy logic systems. The paper presents an initial evaluation of the proposed approach through 52 scenarios with 13 experts to compare their assessments to those of the fuzzy logic decision support system. The results show that the system provides reliable assessments to the security analysts, in particular, generating more conservative assessments in 19% of the test scenarios compared to the experts’ ratings

Expert diagnosis of polymer electrolyte fuel cells

Diagnosing faulty conditions of engineering systems is a highly desirable process within control structures, such that control systems may operate effectively and degrading operational states may be mitigated. The goal herein is to enhance lifetime performance and extend system availability. Difficulty arises in developing a mathematical model which can describe all working and failure modes of complex systems. However the expert's knowledge of correct and faulty operation is powerful for detecting degradation, and such knowledge can be represented through fuzzy logic. This paper presents a diagnostic system based on fuzzy logic and expert knowledge, attained from experts and experimental findings. The diagnosis is applied specifically to degradation modes in a polymer electrolyte fuel cell. The defined rules produced for the fuzzy logic model connect observed operational modes and symptoms to component degradation. The diagnosis is then tested against common automotive stress conditions to assess functionality

Implementation of Pre Compensation Fuzzy For a Cascade PID Controller Using Matlab Simulink

Fuzzy logic control technology has been widely and successfully utilized in numerous industrial applications. Since fuzzy logic with humanlike but systematic properties can convert linguistic control rule based on expert knowledge into automatic control strategies, it can be well applied to control the process with un modeled and nonlinear dynamics. In this paper a fuzzy logic based pre compensation scheme for PID controller is proposed. Fuzzy methods can be used effectively to implement conventional control methods for performance improvement. The scheme is based on graphically studying the source of steady state errors arising from applying PID type schemes of systems with dead zones. This work is based on trying to compensate for overshoot and undershoot by the transient response. This is easy to implement in practice since an existing PID controller can be used in conjunction with the fuzzy pre compensator without modification Keywords: Fuzzy controller, PID Controller ,Fuzzification, Defuzzification

Nursing And Fuzzy Logic: An Integrative Review.

This study conducted an integrative review investigating how fuzzy logic has been used in research with the participation of nurses. The article search was carried out in the CINAHL, EMBASE, SCOPUS, PubMed and Medline databases, with no limitation on time of publication. Articles written in Portuguese, English and Spanish with themes related to nursing and fuzzy logic with the authorship or participation of nurses were included. The final sample included 21 articles from eight countries. For the purpose of analysis, the articles were distributed into categories: theory, method and model. In nursing, fuzzy logic has significantly contributed to the understanding of subjects related to: imprecision or the need of an expert; as a research method; and in the development of models or decision support systems and hard technologies. The use of fuzzy logic in nursing has shown great potential and represents a vast field for research.19195-20

The development of in-process surface roughness prediction systems in turning operation using accelerometer

Three in-process surface roughness prediction (ISRP) systems using linear multiple regression, fuzzy logic, and fuzzy nets algorisms, respectively, were developed to allow the prediction of real time surface roughness of a work piece on a turning operation. The surface roughness is predicted from feed rate, spindle speed, depth of cut, and machining vibration that is detected and collected by an accelerometer.;Two groups of data were collected for two cutters with nose radii of 0.016 and 0.031 inches, respective. A total of 162 training data sets and 54 testing data sets for each cutter were applied to train and test the system. While the multiple-regression-based system applied the linear relationships of the dependent variables and the dependent variable for the prediction, the fuzzy-logic-based and the fuzzy-nets-based systems relied on fuzzy theory for the prediction. The fuzzy rule banks employed in the fuzzy-logic-based system was generated with expert\u27s experiences as well as observation results from the experiments. Whereas, the rule banks employed in the fuzz-nets-system were rule banks self-extracted from the training data by the fuzzy-nets self-learning algorithm.;The predicted surface roughness values were compared with corresponding measured values. The average prediction accuracy with the three algorithms, linear multiple regression, fuzzy logic, and fuzzy nets algorisms, was 92.78%, 89.06%, and 95.70%, respectively. The use of the accelerometer was found valuable in increasing the prediction The Fuzzy-nets-based In-process Surface Roughness Prediction System was considered the best among the three tested systems. This conclusion relies on not only the best average prediction accuracy achieved, but also the self-learning ability of the fuzzy nets algorism