Fuzzy-Sliding Mode Control of a Polishing Robot Based on Genetic Algorithm

Go, Seok-Jo,Lee, Min-Cheol,Park, Min-Kyu The Korean Society of Mechanical Engineers 2001 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.15 No.5

This paper proposes a fuzzy-sliding mode control which is designed by a self tuning fuzzy inference method based on a genetic algorithm. Using the method, the number of inference rules and the shape of the membership functions of the proposed fuzzy-sliding mode control are optimized without the aid of an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method. It is further guaranteed that the selected solution becomes the global optimal solution by optimizing Akaikes information criterion expressing the quality of the inference rules. In order to evaluate the learning performance of the proposed fuzzy-sliding mode control based on a genetic algorithm, a trajectory tracking simulation of the polishing robot is carried out. Simulation results show that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the trajectory control result is similar to the result of the fuzzy-sliding mode control which is selected through trial error by an expert. Therefore, a designer who does not have expert knowledge of robot systems can design the fuzzy-sliding mode controller using the proposed self tuning fuzzy inference method based on the genetic algorithm.

Promethean Fuzzy Model to Predict Diabetic Foot Ulcer

J.Jayashree,Nikhil Chaudhari,N.Ch.S.N. Iyengar 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.11

One of the most serious complications of diabetes is Diabetic foot. The incidence of diabetic foot increases equally on a rise of diabetic's increases and it is often ignored by people leading to a rise in major amputations. Diabetic foot disease diagnosis is done by using fuzzy logic and beneficial as it incorporates the knowledge and experience of physician into fuzzy sets and rules. In this paper, we propose a fuzzy expert system framework for diabetic foot which constructs large scale knowledge based diabetic foot system. The knowledge is constructed by using the fuzzification. Fuzzy verdict mechanism uses triangular membership function with mamdanis inference. Defuzzification method is adopted to convert the fuzzy values into crisp values.

Analyzing Inference and Functional Dependency in Fuzzy Database Systems

황재천,이광규 경원전문대학 1996 論文集 Vol.18 No.2

본 논문은 퍼지관계 데이타베이스에서 추론을 다루고 함수형 종속과 퍼지추론 채널의 중요한 개념을 정의 하는데 중점을 두었다. 이와 같은 분석은 퍼지관계 하에서의 명백한 주관관계와 검색을 하기 위한 특수한 추상적인 모델을 이용하여 수행이 되고 또한, 중요한 데이타베이스 무결성의 성질들이 추론모델에서 정의가 된다. 그와 같은 성질들은 기존의 데이터베이스 모델과 퍼지관계 데이터베이스 모델을 밀접하게 연관시킨다. 이들 둘 사이의 관계는 데이타베이스 보안, 지식발견과 같은 응용문제에서 퍼지관계 데이터베이스 시스템이 추론형식의 유틸리티를 개발할 수 있게 해준다. 본 논문은 기존의 관계 데이타베이스를 이용하여 퍼지관계 데이터베이스에서의 Contex를 적용하여 지식을 표현하는 것을 보여준다. This paper deals with. inference in fuzzy relational databases, It focuss on functional dependencies(FDs) and defines the important notion of a fuzzy inference channel. The analysis is performed using a special abstract model which captures and articulates subjective equivalences underying fuzziness. Core database integrity properties are defined for the abstract model. They preserve a vital link to the classical model and to existing fuzzy relational database models. The link increases the utility of the inference formalism in applications such as database security and knowledge discovery; these applications are discussed in the Con-text of fuzzy relational database.

Fuzzy Inference Mechanism Based on Fuzzy Cognitive Map for B2B Negotiation

Kun Chang Lee,Byung Uk Kang 한국전자거래학회 2004 한국전자거래학회 학술대회 발표집 Vol.2004 No.-

This paper is aimed at proposing a fuzzy inference mechanism to enhancing the quality of cognitive map-based inference. Its main virtue lies in the two mechanisms: (1) a mechanism for avoiding a synchronization problem which is often observed during inference process with traditional cognitive map, and (2) a mechanism for fuzzifying decision maker's subjective judgment. Our proposed fuzzy inference mechanism (FIM) is basically based on the cognitive map stratification algorithm which can stratify a cognitive map into number of strata and then overcome the synchronization problem successfully. Besides, the proposed FIM depends on fuzzy membership function which is administered by decision maker. With an illustrative B2B negotiation problem, we applied the proposed FIM, deducing theoretical and practical implications. Implementation was conducted by Matlab language.

개별 입력 공간에 의한 퍼지 추론 시스템의 비선형 특성

박건준(Park, Keon-Jun),이동윤(Lee, Dong-Yoon) 한국산학기술학회 2011 한국산학기술학회논문지 Vol.12 No.11

비선형 공정에 대한 퍼지 모델링은 일반적으로 주어진 데이터를 이용하여 입력 변수를 선정하고 각 입력 변 수에 대한 입력 공간을 분할하여 이들 입력 변수 및 공간 분할에 의해 퍼지 규칙을 형성한다. 퍼지 규칙의 전반부는 입력 변수 선정, 공간 분할 수 및 소속 함수에 의해 동정되고 퍼지 규칙의 후반부는 간략 추론, 선형 추론에 의해 다 항식 함수의 형태로 동정된다. 일반적으로 주어진 데이터를 이용한 비선형 공정에 대한 퍼지 규칙의 형성은 차원이 증가할수록 규칙의 수가 지수적으로 증가하는 문제를 가지고 있다. 이를 해결하기 위해 각 입력 공간의 퍼지 분할에 의한 퍼지 규칙을 개별적으로 형성함으로써 복잡한 비선형 공정을 모델링 할 수 있다. 따라서 본 논문에서는 개별적 인 입력 공간을 활용하여 퍼지 규칙을 생성한다. 퍼지 규칙의 전반부 파라미터는 입력 데이터의 최소 값과 최대 값을 이용하는 최소-최대 방법을 이용하여 동정되고, 소속 함수는 삼각형, 범종형, 사다리꼴형 소속 함수를 사용한다. 마지 막으로, 비선형 공정으로는 널리 이용되는 데이터를 이용하여 시스템 특성 및 성능을 평가한다. In fuzzy modeling for nonlinear process, typically using the given data, the fuzzy rules are formed by the input variables and the space division by selecting the input variable and dividing the input space for each input variables. The premise part of the fuzzy rule is identified by selection of the input variables, the number of space division and membership functions and the consequent part of the fuzzy rule is identified by polynomial functions in the form of simplified and linear inference. In general, formation of fuzzy rules for nonlinear processes using the given data have the problem that the number of fuzzy rules exponentially increases. To solve this problem complex nonlinear process can be modeled by separately forming the fuzzy rules by means of fuzzy division of each input space. Therefore, this paper utilizes individual input space to generate fuzzy rules. The premise parameters of the fuzzy rules are identified by Min-Max method using the minimum and maximum values of input data set and membership functions are used as a series of triangular, gaussian-like, trapezoid-type membership functions. And lastly, using the data which is widely used in nonlinear process we evaluate the performance and the system characteristics.

An Emotion Classification Based on Fuzzy Inference and Color Psychology

Chang-Sik Son,Hwan-Mook Chung 한국지능시스템학회 2004 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.4 No.1

It is difficult to understand a person's emotion, since it is subjective and vague. Therefore, we are proposing a method by which will effectively classify human emotions into two types (that is, single emotion and composition emotion). To verify validity of te proposed method, we conducted two experiments based on general inference and a- cut, and compared the experimental results. In the first experiment emotions were classified according to fuzzy inference. On the other hand in the second experiment emotions were classified accroding to a-cut. Our experimental results showed that the classification of emotion based on a - cut was more definite that that based on fuzzy inference.

Genetically Optimized Hybrid Fuzzy Set-based Polynomial Neural Networks with Polynomial and Fuzzy Polynomial Neurons

Sung-Kwun Oh,Seok-Beom Roh,Keon-Jun Park 한국지능시스템학회 2005 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.5 No.4

We investigatea new fuzzy-neural networks-Hybrid Fuzzy set based polynomial Neural Networks (HFSPNN). These networks consist of genetically optimized multi-layer with two kinds of heterogeneous neurons thatare fuzzy set based polynomial neurons (FSPNs) and polynomial neurons (PNs). We have developed a comprehensive design methodology to determine the optimal structure of networks dynamically. The augmented genetically optimized HFSPNN (namely gHFSPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional HFPNN. The GA-based design procedure being applied at each layer of gHFSPNN leads to the selection leads to the selection of preferred nodes (FSPNs or PNs) available within the HFSPNN. In the sequel, the structural optimization is realized via GAs, whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gHFSPNN is quantified through experimentation where we use a number of modeling benchmarkssynthetic and experimental data already experimented with in fuzzy or neurofuzzy modeling.

Genetically Optimized Hybrid Fuzzy Set-based Polynomial Neural Networks with Polynomial and Fuzzy Polynomial Neurons

Oh Sung-Kwun,Roh Seok-Beom,Park Keon-Jun Korean Institute of Intelligent Systems 2005 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.5 No.4

We investigatea new fuzzy-neural networks-Hybrid Fuzzy set based polynomial Neural Networks (HFSPNN). These networks consist of genetically optimized multi-layer with two kinds of heterogeneous neurons thatare fuzzy set based polynomial neurons (FSPNs) and polynomial neurons (PNs). We have developed a comprehensive design methodology to determine the optimal structure of networks dynamically. The augmented genetically optimized HFSPNN (namely gHFSPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional HFPNN. The GA-based design procedure being applied at each layer of gHFSPNN leads to the selection leads to the selection of preferred nodes (FSPNs or PNs) available within the HFSPNN. In the sequel, the structural optimization is realized via GAs, whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gHFSPNN is quantified through experimentation where we use a number of modeling benchmarks synthetic and experimental data already experimented with in fuzzy or neurofuzzy modeling.

Design of Fuzzy-Sliding Model Control with the Self Tuning Fuzzy Inference Based on Genetic Algorithm and Its Application

Go, Seok-Jo,Lee, Min-Cheol,Park, Min-Kyn Institute of Control 2001 Transaction on control, automation and systems eng Vol.3 No.1

This paper proposes a self tuning fuzzy inference method by the genetic algorithm in the fuzzy-sliding mode control for a robot. Using this method, the number of inference rules and the shape of membership functions are optimized without an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method. And, it is guaranteed that he selected solution become the global optimal solution by optimizing the Akaikes information criterion expressing the quality of the inference rules. The trajectory tracking simulation and experiment of the polishing robot show that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the proposed fuzzy-sliding mode controller provides reliable tracking performance during the polishing process.

퍼지추론을 이용한 최적의 얼굴검출 알고리즘 선택기법

장대식(Dae-Sik Jang) 한국컴퓨터정보학회 2011 韓國컴퓨터情報學會論文誌 Vol.16 No.1

본 논문에서는 퍼지추론을 기반으로 얼굴검출 알고리즘을 지능적으로 선택함으로써 개발자들이 전문적인 지식이 없이 얼굴검출 기능을 손쉽게 사용할 수 있는 새로운 기법을 제안한다. 본 논문의 목적은 퍼지추론 기반의 고차원 얼굴검출 시스템을 제시함으로써 사용자들이 컴퓨터비전 이론이나 개별 알고리즘들에 대한 전문적인 지식이 없어도 손쉽게 얼굴검출 기능을 포함하는 시스템을 개발할 수 있도록 지원하는데 있다. 얼굴검출의 방대한 문제영역을 분류하기 위해서 가장 먼저 얼굴검출을 위한 주요한 조건들을 고려하고 정리하였다. 이렇게 정리된 조건들은 개발자들이 주어진 문제를 표현하는데 사용할 수 있도록 정의되었다. 정의된 조건들과 사용 가능한 얼굴검출 알고리즘들은 퍼지추론 규칙을 이용하여 규칙화 되고 퍼지추론 해석기를 구성한다. 개발자들에 의해서 개별 문제의 조건들이 정리되면, 제안된 퍼지해석기가 퍼지추론을 통해 이에 대응되는 문제를 해결하기 위한 최적을 알고리즘들을 찾아내고 구성한다. 제안된 방법의 개념검증을 위해 기존의 알고리즘들과 성능을 비교하였으며 이를 분석하고 우수성과 실용성을 보여준다. This paper provides a novel approach for developers to use face detection techniques for their applications easily without special knowledge by selecting optimal face detection algorithms based on fuzzy inference. The purpose of this paper is to come up with a high-level system for face detection based on fuzzy inference with which users can develop systems easily and even without specific knowledge on face detection theories and algorithms. Important conditions are firstly considered to categorize the large problem space of face detection. The conditions identified here are then represented as expressions so that developers can use them to express various problems. The expressed conditions and available face detection algorithms constitute the fuzzy inference rules and the Fuzzy Interpreter is constructed based on the rules. Once the conditions are expressed by developers, the Fuzzy Interpreter proposed take the role to inference the conditions and find and organize the optimal algorithms to solve the represented problem with corresponding conditions. A proof-of-concept is implemented and tested compared to conventional algorithms to show the performance of the proposed approach.