• International Journal of Fuzzy Logic and Intelligent Systems
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• 제15권4호
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• pp.217-231
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• 2015

A novel fuzzy learning framework that employs fuzzy inference to solve the problem of Multiple Instance Learning (MIL) is presented. The framework introduces a new class of fuzzy inference systems called Multiple Instance Mamdani Fuzzy Inference Systems (MI-Mamdani). In multiple instance problems, the training data is ambiguously labeled. Instances are grouped into bags, labels of bags are known but not those of individual instances. MIL deals with learning a classifier at the bag level. Over the years, many solutions to this problem have been proposed. However, no MIL formulation employing fuzzy inference exists in the literature. Fuzzy logic is powerful at modeling knowledge uncertainty and measurements imprecision. It is one of the best frameworks to model vagueness. However, in addition to uncertainty and imprecision, there is a third vagueness concept that fuzzy logic does not address quiet well, yet. This vagueness concept is due to the ambiguity that arises when the data have multiple forms of expression, this is the case for multiple instance problems. In this paper, we introduce multiple instance fuzzy logic that enables fuzzy reasoning with bags of instances. Accordingly, a MI-Mamdani that extends the standard Mamdani inference system to compute with multiple instances is introduced. The proposed framework is tested and validated using a synthetic dataset suitable for MIL problems. Additionally, we apply the proposed multiple instance inference to fuse the output of multiple discrimination algorithms for the purpose of landmine detection using Ground Penetrating Radar.

• 한국지능시스템학회논문지
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• 제22권4호
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• pp.521-526
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• 2012

제조공정을 통해 생산된 화살의 성능은 화살의 이동궤적(궁사의 패러독스)과 탄착점의 집적도에 따라 좌우된다. 특히 동일한 환경에서 반복적으로 화살의 슈팅실험을 수행할 경우, 반복실험에서 얻어진 화살의 탄착점 집적도는 화살 성능 평가에서 중요한 객관적 지표가 된다. 그러나 화살의 탄착점에 대한 분석은 현재 상용화된 기술이 부족하며, 기존의 연구들은 화살의 성능에 영향을 미치는 제조공정 변수(화살깃, 화살촉, 화살의 곧기, 중량, 외경, 스파인)만을 최적화하려는 방향으로 기술력이 집중되어 있다. 본 논문에서는 화살의 주요성능지표인 화살의 탄착점 측정 자동화를 위해, Mamdani 퍼지 추론 시스템(Mamdani Fuzzy Inference System)과 도형의 닮음(Similarity of Polygon)을 이용한 화살의 탄착점 측정 시스템을 제안한다. 라인레이저(Line Laser)와 포토다이오드어레이(Photo Diode Array)를 이용하여 고속(약 275km/h)으로 이동하는 화살의 탄착점 데이터를 계측하고, 계측된 데이터를 퍼지 추론과 도형의 닮음을 이용하여 화살의 탄착점으로 사상(Mapping) 시킨다.

• 한국정보통신학회논문지
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• 제13권3호
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• pp.505-513
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• 2009

Z. cao는 Relation matrix를 사용한 정밀한 추론이 가능한 NFRM(New fuzzy reasoning method)을 제안하였다. 이는 추론의 규칙 수가 적음에도 불구하고 Mamdani의 퍼지 추론방식에 비하여 좋은 성능을 보였다. 그러나 대부분의 퍼지스템의 경우, MIMO 시스템에 적용 시 퍼지 추론규칙을 도출해 내기 힘들고 많은 규칙의 수가 요구되는 단점을 갖는다. 그러므로 본 연구자에 의하여 과거에 Z. Cao's의 퍼지 추론방법을 MIMO 시스템으로 확장된 MIMO 퍼지추론 방식이 제안되었다. 그러나 정밀한 추론을 위하여 relation matrix는 휴리 스틱 (heuristic)한 방법이나 시행착오법을 사용하여 구하였고, 이는 많은 시간과 노력이 필요하다. 본 연구에서는 이러한 relation matrix를 구하기 위하여 시행 착오법에 의해 소요되는 많은 시간과 노력을 줄이고, 더욱 정밀한 추론 성능의 개선을 위하여 경사감소학습법을 사용한 학습기 능을 갖는 MIMO 퍼지추론 방식을 제안하고자 한다. 모의실험은 2축 로봇의 역기구학 문제를 푸는데 적용하여 제안된 추론방식이 좋은 성능을 보였다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2008년도 추계종합학술대회 B
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• pp.175-178
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• 2008

Z. cao는 Relation matrix를 사용한 정밀한 추론이 가능한 NFRM(New fuzzy reasoning method)을 제안하였다. 이는 추론의 규칙 수가 적음에도 불구하고 Mamdani의 퍼지추론 방식에 비하여 좋은 성능을 보였다. 그러나 대부분의 퍼지스템의 경우, MIMO 시스템에 적용시 피지추론규칙을 도출해 내기 힘들고 많은 규칙의 수가 요구되는 단점을 갖는다. 그러므로 본 연구자에 의하여 과거에 Z. Cao's의 퍼지추론 방법을 MIMO 시스템으로 확장된 MIMO 퍼지추론 방식을 제안하였다. 본 연구에서는 제안된 퍼지추론 방식의 relation matrix를 시행착오법에 의해 소요되는 많은 시간과 노력을 줄이고, 더욱 정밀한 추론 성능의 개선을 위하여 경사감소학습법을 사용한 학습기능을 갖는 MIMO 퍼지추론 방식을 제안하고자 한다. 모의실험은 2축 로봇의 역기구학 문제를 푸는데 적용하여 제안된 추론방식이 좋은 성능을 보였다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2008년도 춘계종합학술대회 A
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• pp.193-196
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• 2008

과거 Z. cao는 Relation matrix를 사용한 정밀한 추론이 가능한 NFRM(New fuzzy reasoning method)을 제안하였다. 이는 추론의 규칙 수가 적음에도 불구하고 Mamdani의 퍼지추론방식에 비하여 좋은 성능을 보였다. 그러나 정밀한 추론을 위하여 relation matrix는 시행착오법을 사용하여 구하고, 이는 많은 시간과 노력이 필요하다. 본 연구에서는 이러한 relation matrix를 구하기 위하여 시행착오법에 의해 소요되는 많은 시간과 노력을 줄이고, 더욱 정밀한 추론 성능의 개선을 위하여 경사감소학습법을 사유한 학습기능을 갖는 Z. Cao의 퍼지추론 방식을 제안하고자 한다.

• 한국융합학회논문지
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• 제5권1호
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• pp.35-41
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• 2014

An automatic speech recognition system is one of the popular research problems. There are many research groups working in this field for different language including Japanese. Japanese vowel recognition is one of important parts in the Japanese speech recognition system. The vowel classification system with the Mamdani fuzzy inference system was developed in this research. We tested our system on the blind test data set collected from one male native Japanese speaker and four male non-native Japanese speakers. All subjects in the blind test data set were not the same subjects in the training data set. We found out that the classification rate from the training data set is 95.0 %. In the speaker-independent experiments, the classification rate from the native speaker is around 70.0 %, whereas that from the non-native speakers is around 80.5 %.

• 대한한의진단학회지
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• 제19권3호
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• pp.141-149
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• 2015

Objectives Recently the fuzzy logic is widely used in the decision making, identification, pattern recognition, optimization in various fields. In this study, we propose the fuzzy logic as the objective method of distinguishing hot and cold, the basis of diagnosis in Korean medicine. Methods We developed fuzzy inference system to distinguish whether the subjects had hot or cold. The cold and hot questionnaire of Korean traditional university textbook, the pulse rate and the DITI value of face used in the system. These three kinds of information were defined as 'fuzzy sets,' and 54 fuzzy rules were established on the basis of clinical practitioners' knowledge. The fuzzy inference was performed by using the Mamdani's method. To evaluate the usefulness of the fuzzy inference system, 200 cases of data measured in the Woosuk university hospital of oriental medicine were used to compare the determining hot, normal, cold results obtained from the experts and from the proposed system. Results As a result, 100 cases of "cold", 54 cases of "normal", and 34 cases of "hot" were matched between the experts and the proposed system. This fuzzy system showed the conformity degree of 94%(${\kappa}=0.853$). Conclusions In this study, we could express the process of distinguishing hot-cold using the fuzzy logic for objectification and quantification of hot-cold identification. This is the first study that introduce a fuzzy logic for distinguish pattern identification. The degree of the heat characteristic of the patients inferred by this system could provide a more objective basis for diagnosing the hot-cold of patients.

• 전자공학회논문지B
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• 제29B권7호
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• pp.30-41
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• 1992

As a general model of rule-based systems, we propose a model for a fuzzy production system having chaining rules and an inference engine associated with the model. The concept of so-called 'fuzzy petri nets' is used to model the fuzzy production system and the inference engine is designed to be capable of handling inexact knowledge. The fuzzy logic is adopted to represent vagueness in the rules and the certainty factor is used to express uncertainty of each rules given by a human expert. Parallel, inference schemes are devised by transforming Fuzzy Petri nets to matrix formula. Futher, the inference engine mechanism under the Mamdani's implication method can be desceribed by a simple algebraic formula, which makes real time inference possible.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.999-1004
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• 2005

An algorithm for a hybrid controller consists of a sliding mode control part and a fuzzy logic part which ar purposely for nonlinear systems. The sliding mode part of the solution is based on "eigenvalue/vector"-type controller is used as the backstepping approach for tracking errors. The fuzzy logic part is a Mamdani fuzzy model. This is designed by applying sliding mode control (SMC) method to the dynamic model. The main objective is to keep the update dynamics in a stable region by used SMC. After that the plant behavior is presented to train procedure of adaptive neuro-fuzzy inference systems (ANFIS). ANFIS architecture is determined and the relevant formulation for the approach is given. Using the error (e) and rate of error (de), occur due to the difference between the desired output value (yd) and the actual output value (y) of the system. A dynamic adaptation law is proposed and proved the particularly chosen form of the adaptation strategy. Subsequently VSC creates a sliding mode in the plant behavior while the parameters of the controller are also in a sliding mode (stable trainer). This study considers the ANFIS structure with first order Sugeno model containing nine rules. Bell shaped membership functions with product inference rule are used at the fuzzification level. Finally the Mamdani fuzzy logic which is depends on adaptive neuro-fuzzy inference systems structure designed. At the transferable stage from ANFIS to Mamdani fuzzy model is adjusted for the membership function of the input value (e, de) and the actual output value (y) of the system could be changed to trapezoidal and triangular functions through tuning the parameters of the membership functions and rules base. These help adjust the contributions of both fuzzy control and variable structure control to the entire control value. The application example, control of a mass-damper system is considered. The simulation has been done using MATLAB. Three cases of the controller will be considered: for backstepping sliding-mode controller, for hybrid controller, and for adaptive backstepping sliding-mode controller. A numerical example is simulated to verify the performances of the proposed control strategy, and the simulation results show that the controller designed is more effective than the adaptive backstepping sliding mode controller.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1274-1276
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• 1993

We have designed a multiple-valued fuzzy Approximate Analogical-Reseaning system (AARS). The system uses a similarity measure of fuzzy sets and a threshold of similarity ST to determine whether a rule should be fired, with a Modification Function inferred from the Similarity Measure to deduce a consequent. Multiple-valued basic fuzzy blocks are used to construct the system. A description of the system is presented to illustrate the operation of the schema. The results of simulations show that the system can perform about 3.5 x 106 inferences per second. Finally, we compare the system with Yamakawa's chip which is based on the Compositional Rule of Inference (CRI) with Mamdani's implication.