• 제어로봇시스템학회논문지
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• 제2권3호
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• pp.148-157
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• 1996

Availability of input trajectories corresponding to desired output trajectories is often important in designing control systems for batch and other transient processes. In this paper, we propose a predictive control-type model-based iterative learning algorithm which is applicable to finding the nominal input trajectories of a linear time-invariant batch process. Unlike the other existing learning control algorithms, the proposed algorithm can be applied to nonsquare systems and has an ability to adjust noise sensitivity as well as convergence rate. A simple model identification technique with which performance of the proposed learning algorithm can be significantly enhanced is also proposed. Performance of the proposed learning algorithm is demonstrated through numerical simulations.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2011년도 한국컴퓨터종합학술대회논문집 Vol.38 No.1(A)
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• pp.302-304
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• 2011

Rule based machine learning techniques are very important in our real world now. We can list out some important application which we can apply rule based machine learning algorithm such as medical data mining, business transaction mining. The different between rules based machine learning and model based machine learning is that model based machine learning out put some models, which often are very difficult to understand by expert or human. But rule based techniques output are the rule sets which is in IF THEN format. For example IF blood pressure=90 and kidney problem=yes then take this drug. By this way, medical doctor can easy modify and update some usable rule. This is the scenario in medical decision support system. Currently, Rough set is one of the most famous theory which can be used for produce the rule. LEM2 is the algorithm use this theory and can produce the small set of rule on the database. In this paper, we present an improvement of LEM2 algorithm which incorporates the variable precision techniques.

• 전자공학회논문지SC
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• 제39권2호
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• pp.85-94
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• 2002

본 논문에서는 CITE를 포함한 2차 반복 학습제어 방법이 수렴 성능의 향상과 외란에 대한 강인성 향상에 덧붙여 초기 오차가 있음에도 불구하고 이를 극복할 뿐만 아니라 기존의 알고리즘보다 더 빠른 수렴 능력이 있음을 확인한다. 또한 불안정한 결과를 낳는 높은 학습 게인의 경우에도 CITE를 추가한 본 학습제어 방법에 의해 안정화됨으로써, 빠른 수렴 특성과 강인성 향상을 가져올 수 있음을 보인다. 그리고 본 알고리즘을 선형 시변 시스템에 대해 적용한 시뮬레이션 결과를 통해 초기 오차의 극복 능력이 뛰어남을 확인하고, 아울러 각 학습 게인들이 수렴 속도와 안정성에 미치는 영향을 상세히 분석한다.

• 한국멀티미디어학회논문지
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• 제19권2호
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• pp.325-333
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• 2016

The development of smart technology has brought the conversion of closed traditional e-learning contents into open flexible smart learning contents consisting of learner-centered modules, without the constraints of time and space by use of smart devices from the uniformed and passive classroom between teachers and learners. It has been demanded an open, personalized and customized teaching and learning contents of smart education and training systems according to wide supply of various smart devices. In this paper, we discuss about the status of the smart teaching and learning systems and analyze the characteristics and structure of the web contents for smart education and training systems by use of smart devices. And we propose a method how to block web contents, to extract them, and adapt personalized segments of web contents by adaptive algorithm into smart learning devices. We extract blocks from the web contents based on the smart device information and the preference information of the learners from existing web contents without the hassle of learners environment. After specifying a block priority from the extracted web contents by the adaptive segment algorithm, it can be displayed directly to the screen to fit the individual learning progress of the learners.

• 한국지능시스템학회논문지
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• 제14권2호
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• pp.216-221
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• 2004

This paper describes an optimal synthesis method of binary neural network for pattern recognition. Our objective is to minimize the number of connections and the number of neurons in hidden layer by using a Newly Expanded and Truncated Learning Algorithm (NETLA) for the multilayered neural networks. The synthesis method in NETLA uses the Expanded Sum of Product (ESP) of the boolean expressions and is based on the multilayer perceptron. It has an ability to optimize a given binary neural network in the binary space without any iterative learning as the conventional Error Back Propagation (EBP) algorithm. Furthermore, NETLA can reduce the number of the required neurons in hidden layer and the number of connections. Therefore, this learning algorithm can speed up training for the pattern recognition problems. The superiority of NETLA to other learning algorithms is demonstrated by an practical application to the approximation problem of a circular region.

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.148-157
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• 2021

The effectiveness of recommendation systems depends on the performance of the algorithms with which these systems are designed. The quality of the algorithms themselves depends on the quality of the strategies with which they were designed. These strategies differ from author to author. Thus, designing a good recommendation system means implementing the good strategies. It's in this context that several research works have been proposed on various strategies applied to algorithms to meet the needs of recommendations. Researchers are trying indefinitely to address this objective of seeking the qualities of recommendation algorithms. In this paper, we propose a new algorithm for recommending learning items. Learner performance predictions and collaborative recommendation methods are used as strategies for this algorithm. The proposed performance prediction model is based on convolutional neural networks (CNN). The results of the performance predictions are used by the proposed recommendation algorithm. The results of the predictions obtained show the efficiency of Deep Learning compared to the k-nearest neighbor (k-NN) algorithm. The proposed recommendation algorithm improves the recommendations of the learners' learning items. This algorithm also has the particularity of dissuading learning items in the learner's profile that are deemed inadequate for his or her training.

• Journal of the Korean Data and Information Science Society
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• 제15권2호
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• pp.477-483
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• 2004

One of the major paradigms for supervised learning in neural network community is back-propagation learning. The standard implementations of back-propagation learning are optimal under the assumptions of identical and independent Gaussian noise. In this paper, for regression function estimation, we introduce $\varepsilon-insensitive$ back-propagation learning algorithm, which corresponds to minimizing the least absolute error. We compare this algorithm with support vector machine(SVM), which is another $\varepsilon-insensitive$ supervised learning algorithm and has been very successful in pattern recognition and function estimation problems. For comparison, we consider a more realistic model would allow the noise variance itself to depend on the input variables.

• Structural Engineering and Mechanics
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• 제76권1호
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• pp.101-114
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• 2020

In this paper, an improved experience-based learning algorithm (EBL), termed as IEBL, is proposed to solve the nonlinear hysteretic parameter identification problem with Bouc-Wen model. A quasi-opposition-based learning mechanism and new updating equations are introduced to improve both the exploration and exploitation abilities of the algorithm. Numerical studies on a single-degree-of-freedom system without/with viscous damping are conducted to investigate the efficiency and robustness of the proposed algorithm. A laboratory test of seven lead-filled steel tube dampers is presented and their hysteretic parameters are also successfully identified with normalized mean square error values less than 2.97%. Both numerical and laboratory results confirm that, in comparison with EBL, CMFOA, SSA, and Jaya, the IEBL is superior in nonlinear hysteretic parameter identification in terms of convergence and accuracy even under measurement noise.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.399-403
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• 1998

In this paper, we proposed a new fuzzy supervised learning algorithm. We construct, and train, a new type fuzzy neural net to model the linear activation function. Properties of our fuzzy neural net include : (1) a proposed linear activation function ; and (2) a modified delta rule for learning algorithm. We applied this proposed learning algorithm to exclusive OR,3 bit parity using benchmark in neural network and pattern recognition problems, a kind of image recognition.

• 정보교육학회논문지
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• 제20권2호
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• pp.151-160
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• 2016

알고리즘 교육은 컴퓨터과학 교육의 기본 원리를 가르치는 도구로서 강조되고 한다. 그러나 초등학생에 적합한 알고리즘 교재자료는 매우 부족한 상태이다. 본 연구에서는 초등학생들이 알고리즘에 대해 쉽게 배울 수 있도록 컴퓨터과학 언플러그드의 내용을 기반으로 교육자료를 제시하였다. 학습자의 자발적 학습활동을 위한 문제 해결 탐구과정을 제시하고, 학생들은 개별 또는 조별 활동중심학습으로 구성하였다. 생활 속의 문제를 해결하는 알고리즘 학습을 위해 기본적인 검색과 정렬 알고리즘들을 바탕으로 해싱기법의 교수법 및 교육자료 개발하였다. 본 연구에서 제시한 교육자료는 전문가 집단의 설문 분석을 통해 적절하다는 결론을 얻었다.