• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.301-304
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• 2007

학습법칙은 신경회로망의 성능에 중요한 영향을 미친다. 본 논문은 데이터와 클러스터들의 대표값들 사이의 거리를 고려하여 학습률을 정하는 새로운 퍼지 학습법칙을 제안한다. 클러스터들의 대표값을 조정할 때, 이러한 고려는 outlier에 비하여 결정경계선 근처에 있는 데이터의 반영도를 높임으로써 outlier의 클러스터의 대표값에 미치는 영향도를 낮출 수 있다. 따라서 outlier들이 결정경계선을 악화시키는 것을 방지할 수 있다. 이 새로운 퍼지 학습법칙을 IAFC(Integrated Adaptive Fuzzy Clustering) 신경회로망에 적용하였다. 제안한 퍼지 신경회로망과 다른 감독 신경회로망들의 성능을 비교하기 위하여 iris 데이터를 사용하였다. iris 데이터를 사용하여 테스트한 결과 제안한 퍼지 신경회로망의 성능이 우수함을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.1
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• pp.99-118
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• 2013

Electrocardiograms (ECGs) are widely used by clinicians to identify the functional status of the heart. Thus, there is considerable interest in automated systems for real-time monitoring of arrhythmia. However, intra- and inter-patient variability as well as the computational limits of real-time monitoring poses significant challenges for practical implementations. The former requires that the classification model be adjusted continuously, and the latter requires a reduction in the number and types of ECG features, and thus, the computational burden, necessary to classify different arrhythmias. We propose the use of adaptive learning to automatically train the classifier on up-to-date ECG data, and employ adaptive feature selection to define unique feature subsets pertinent to different types of arrhythmia. Experimental results show that this hybrid technique outperforms conventional approaches and is therefore a promising new intelligent diagnostic tool.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2747-2749
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• 2005

This paper presents an intelligent control system for an ac servo motor dirve to track periodic commands using a neural network. AC servo motor drive system is rather similar to a linear system. However, the uncertainties, such as machanical parametric variation, external disturbance, uncertainty due to nonideal in transient state. therefore an intelligent control system that isan on-line trained neural network controller with adaptive learning rates.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.121-124
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• 1997

In this paper, applications of self-recurrent neural networks based of adaptive controller to position control of flexible robot link are considered. The self-recurrent neural networks can be used to approximate any continuous function to any desired degree of accuracy and the weights are updated by feedback-error learning algorithm. Therefore, a comparative analysis was mode with linear controller through an simulation. The results are presented to illustrate the advantages and improved performance of the proposed position tracking controller over the conventional linear controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.267-270
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• 1997

As an approach to develope a control system with robustness in changing control environment conditions, this paper will propose a robot manipulator control system using multilayer neural network and immune algorithm. The proposed immune algorithm which has the characteristics of immune system such as distributed and anomaly detection, probabilistic detection, learning and memory, consists of the innate immune algorithm and the adaptive immune algorithm. We will demonstrate the effectiveness of the proposed control system with simulations of a 2-link robot manipulator.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.391-394
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• 2002

진화 연산(Genetic Algorithm)은 최적화 분야에서 사용되는 강력하면서도 일반적인 방법이다. 이러한 진화 연산의 일반성은 진화 연산에서 사용되는 기본 연산자들이 문제에 대한 정보를 필요로 하지 않는 것에 기인하고 있기에, 실제 구현시에는 여러 파라미터들을 문제에 맞게 정해 줌으로써 성능 향상을 죄할 수 있다. 이러한 파라미터의 조절은 보통 시행착오를 거쳐 행해지나, 실행시에 동적으로 파라미터를 학습하는 적응적 진화 연산도 연구되어 왔다. 본 논문에서는 진화 연산에서의 파라미터 학습 과정을 강화 학습 과정으로 공식화하고 강화 학습을 사용한 적응적 진화 연산 구현을 제안한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.5
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• pp.672-676
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• 2010

This paper presents a learning rule that weights more on data near decision boundary. This learning rule generates better decision boundary by reducing the effect of outliers on the decision boundary. The proposed learning rule is integrated into IAFC neural network. IAFC neural network is stable to maintain previous learning results and is plastic to learn new data. The performance of the proposed fuzzy neural network is compared with performances of LVQ neural network and backpropagation neural network. The results show that the performance of the proposed fuzzy neural network is better than those of LVQ neural network and backpropagation neural network.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.2
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• pp.237-242
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• 2003

In this paper, we propose a new adaptive intrusion detection algorithm based on clustering: Kernel-ART, which is composed of the on-line clustering algorithm, ART (adaptive resonance theory), combining with mercer-kernel and concept vector. Kernel-ART is not only satisfying all desirable characteristics in the context of clustering-based IDS but also alleviating drawbacks associated with the supervised learning IDS. It is able to detect various types of intrusions in real-time by means of generating clusters incrementally.

• Journal of Digital Convergence
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• v.12 no.2
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• pp.467-473
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• 2014

Adaptive learning system means a system that provides adaptively learning materials according to the learning needs of learners. It consists of expert model, instructional model and student model. Expert model is that stores information which is to be taught. Student model stores the data of learning history and learning information of students. Instructional model provides necessary learning materials for actual leaners. This paper has constructed student model through learner's profile information and instructional model through dynamic scenario construction. After that, We have developed adaptively to provide learning to learners by constructing suitable dynamic scenario based on learners profile information. In the end, satisfaction result about this system showed a high degree of satisfaction and 88%.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.1113-1119
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• 1989

Neural network control has many innovative potentials for intelligent adaptive control. Among many, it promises real time adaption, robustness, fault tolerance, and self-learning which can be achieved with little or no system models. In this paper, a dynamic robot controller has been developed based on a backpropagation neural network. It gradually learns the robot's dynamic properties through repetitive movements being initially trained with a PD controller. Its control performance has been tested on a simulated PUMA 560 demonstrating fast learning and convergence.