• 전자공학회논문지B
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• 제33B권2호
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• pp.118-130
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• 1996

Fuzzy inference is very useful in expressing ambiguous problems quantitatively and solving them. But like the most of the knowledge based inference systems. It has many difficulties in constructing rules and no learning capability is available. In this paper, we proposed a fuzzy inference system based on fuzy associative memory to solve such problems. The inference system proposed in this paper is mainly composed of learning phase and inference phase. In the learning phase, the system initializes it's basic structure by determining fuzzy membership functions, and constructs fuzzy rules in the form of weights using learning function of fuzzy associative memory. In the inference phase, the system conducts actual inference using the constructed fuzzy rules. We applied the fuzzy inference system proposed in this paper to a pattern classification problem and show the results in the experiment.

• 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.

• 정보과학회 논문지
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• 제42권12호
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• pp.1561-1567
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• 2015

준지도 학습은 기계 학습의 한 분야로서, 레이블된 데이터와 레이블되지 않은 데이터 모두를 사용하여 모델을 학습함으로써 지도 학습에 비해 예측 정확도를 높일 수 있다. 최근 각광받고 있는 그래프 기반 준지도 학습은 입력 데이터를 그래프의 형태로 변환하는 그래프 구축 단계와 이를 사용하여 레이블되지 않은 데이터의 레이블을 예측하는 레이블 추론 단계로 나뉜다. 이 추론은 준지도 학습에서의 평활도 가정을 기본으로 한다. 본 연구에서는 추가로 각 꼭지점 중요도를 결합함으로써 개선된 레이블 추론 알고리즘을 제안한다. 이와 함께 알고리즘의 수렴성을 증명하고, 또한 실험을 통해 알고리즘의 우수성을 검증하였다.

• 한국지능시스템학회논문지
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• 제16권6호
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• pp.742-746
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• 2006

학습자에 대한 학습능력의 평가는 진단평가, 형성평가와 총괄평가 단계로 구분할 수 있다. 이러한 단계적 평가는 학습자의 사전 학습 준비상태부터 학습 과정의 충실성 및 학습 결과까지를 종합적으로 판단할 수 있는 기준이 된다. 본 논문에서는 퍼지추론을 이용하여 각 단계의 평가를 모두 고려한 종합학습평가 방법을 제안하였다. 학습 수행능력에 대한 객관적인 평가를 위하여 각 평가 단계별로 가중치를 부여하였고, 진단, 형성 및 총괄 평가에 대한 퍼지추론에서 획득한 비퍼지화 값을 최종평가의 소속함수 구간으로 적용하였다. 그 결과 객관성을 보장할 수 있는 명확한 추론을 수행할 수 있었으며, 종합적인 학습평가 방법의 타당성을 보였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.96.3-96
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• 2001

In this paper the self-organized and fuzzy inference used stochastic gradient ascent method is proposed. Fuzzy rule and fuzzy set increase as occasion demands autonomously according to the observation information. And two rules(or two fuzzy sets)becoming to be similar each other as progress of learning are unified. This unification causes the reduction of a number of parameters and learning time. Using fuzzy inference and making a rule with an appropriate state division, our proposed method makes it possible to construct a robust reinforcement learning system.

• 제어로봇시스템학회논문지
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• 제10권11호
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• pp.1107-1111
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• 2004

As an information technology is developed, more intelligent system considering emotional factor for implementing the personality is required. In this paper, Reactive Learning Inference System considering emotional factor is proposed. Emotional Facter(E) is defined for a criterion for representing the personal preference. This system is designed to have functions of Reactive filtering by Emotional factor, Incremental learning, perception & inference and knowledge retrieval. This system is applied to the area for analysis of customer's tastes and its performance is analyzed and compared.

• 한국과학교육학회지
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• 제28권8호
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• pp.786-795
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• 2008

The purpose of this research is to propose a program for teaching and learning effective problem-solving for gifted students based on abductive inference. The role of abductive inference is important for scientific discoveries and creative inferences in problem-solving processes. The characteristics of creativity and abductive inference were investigated, and the following were discussed: (a) a suggestion for a new program based on abductive inference for creative outcomes, this program largely consists of two phases: generative hypotheses and confirmative hypotheses, (b) a survey of the validity of a program. It is typical that hypotheses are confirmed in phases through experiments based on hypothetic deductive methodology. However, because generative hypotheses of this hypothetic deductive methodology are not manifest, we maintained that abductive inference strategies must be used in a Creative Teaching-learning Program for gifted science students.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.447-450
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• 2003

In this research, we propose an automatic knowledge acquisition and composite knowledge expression mechanism based on machine learning and relational database. Most of traditional approaches to develop a knowledge base and inference engine of expert systems were based on IF-THEN rules, AND-OR graph, Semantic networks, and Frame separately. However, there are some limitations such as automatic knowledge acquisition, complicate knowledge expression, expansibility of knowledge base, speed of inference, and hierarchies among rules. To overcome these limitations, many of researchers tried to develop an automatic knowledge acquisition, composite knowledge expression, and fast inference method. As a result, the adaptability of the expert systems was improved rapidly. Nonetheless, they didn't suggest a hybrid and generalized solution to support the entire process of development of expert systems. Our proposed mechanism has five advantages empirically. First, it could extract the specific domain knowledge from incomplete database based on machine learning algorithm. Second, this mechanism could reduce the number of rules efficiently according to the rule extraction mechanism used in machine learning. Third, our proposed mechanism could expand the knowledge base unlimitedly by using relational database. Fourth, the backward inference engine developed in this study, could manipulate the knowledge base stored in relational database rapidly. Therefore, the speed of inference is faster than traditional text -oriented inference mechanism. Fifth, our composite knowledge expression mechanism could reflect the traditional knowledge expression method such as IF-THEN rules, AND-OR graph, and Relationship matrix simultaneously. To validate the inference ability of our system, a real data set was adopted from a clinical diagnosis classifying the dermatology disease.

• 한국수학교육학회지시리즈A:수학교육
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• 제34권1호
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• pp.17-63
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• 1995

The exploration of Mathematics-learningmodel on the basis of Cognitive development The purpose of this paper is to sequenctialize Mathematics-learning contents, and to explore teaching-learning model for mathematics, with on the basis of the theory of cognitive development and the period of condservation formation for children. The Specific topics are as follows: (1) Systemizing those theories of cognitive development which are related to Mathematics - learning for children. (2) Organizing a sequence of Mathematics - learning, on the basis of experimental research for the period of conservation formation for children. (3) Comparing the effects of 4 types of teaching - learning model, on the basis of inference activity and operational learning principle. $\circled1$ Induction-operation(IO) $\circled2$ Induction-explanation(IE) $\circled3$ Deduction-operation(DO) $\circled4$ Deduction-explanation(DE) The results of the subjects are as follows: (1) Cognitive development theory and Mathe-matics education. $\circled1$ Congnitive development can be achieved by constant space and Mathematics know-ledge is obtained by the interaction of experience and reason. $\circled2$ The stages of congnitive development for children form a hierarchical system, its function has a continuity and acts orderly. Therefore we need to apply cognitive development for children to teach mathematics systematically and orderly. (2) Sequence of mathematical concepts. $\circled1$ The learning effect of mathematical concepts occurs when this coincides with the period of conservation formation for children. $\circled2$ Mathematics Curriculum of Elementary Schools in Korea matches with the experimental research about the period of Piaget's conservation formation. (3) Exploration of a teaching-learning model for mathematics. $\circled1$ Mathematics learning is to be centered on learning by experience such as observation, operation, experiment and actual measurement. $\circled2$ Mathematical learning has better results in from inductional inference rather than deductional inference, and from operational inference rather than explanatory inference.

• Journal of information and communication convergence engineering
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• 제14권2호
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• pp.97-105
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• 2016

With the increasing demand for interoperability among existing learning resource systems in order to enable the sharing of learning resources, such resources need to be annotated with ontologies that use different metadata standards. These different ontologies must be reconciled through ontology mediation, so as to cope with information heterogeneity problems, such as semantic and structural conflicts. In this paper, we propose an ontology-mapping technique using Semantic Web Rule Language (SWRL) to generate semantic mapping rules that integrate learning resources from different systems and that cope with semantic and structural conflicts. Reasoning rules are defined to support a semantic search for heterogeneous learning resources, which are deduced by rule-based inference. Experimental results demonstrate that the proposed approach enables the integration of learning resources originating from multiple sources and helps users to search across heterogeneous learning resource systems.