• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.11
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• pp.852-858
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• 1991

An adaptive hierarchical classifier(AHCL) for Korean character recognition using a neural net is designed. This classifier has two neural nets: USACL (Unsupervised Adaptive Classifier) and SACL (Supervised Adaptive Classifier). USACL has the input layer and the output layer. The input layer and the output layer are fully connected. The nodes in the output layer are generated by the unsupervised and nearest neighbor learning rule during learning. SACL has the input layer, the hidden layer and the output layer. The input layer and the hidden layer arefully connected, and the hidden layer and the output layer are partially connected. The nodes in the SACL are generated by the supervised and nearest neighbor learning rule during learning. USACL has pre-attentive effect, which perform partial search instead of full search during SACL classification to enhance processing speed. The input of USACL and SACL is a directional edge feature with a directional receptive field. In order to test the performance of the AHCL, various multi-font printed Hangul characters are used in learning and testing, and its processing its speed and and classification rate are compared with the conventional LVQ(Learning Vector Quantizer) which has the nearest neighbor learning rule.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.1
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• pp.94-101
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• 1992

The multilayer perceptron, trained by the error back-propagation learning rule, has been known as a mapping network which can represent arbitrary functions. However depending on the complexity of a function and the initial weights of the multilayer perceptron, the error back-propagation learning may fall into a local minimum or a flat area which may require a long learning time or lead to unsuccessful learning. To solve such difficulties in training the multilayer perceptron by standard error back-propagation learning rule, the paper proposes a learning method which progressively enlarges the learning domain from a small area to the entire region. The proposed method is devised from the investigation on the roles of hidden nodes and connection weights in the multilayer perceptron which approximates a function of one variable. The validity of the proposed method was illustrated through simulations for a function of one variable and a function of two variable with many extremal points.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.598-603
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• 2007

To solve the rule explosion problem in multi-input fuzzy system, a method of converting a given fuzzy system to 2 layered hierarchical fuzzy system has been reported, where at the 1st layer, linearly independent fuzzy rule vectors generated from the given fuzzy system are used and, at the 2nd layer, linear combinations of these independent fuzzy rule vectors are used. In this paper, the steapest descent algorithm is presented to learn the fuzzy rule vectors and related coefficients for the equivalent 2 layered hierarchical structure. By simulation of learning of ball and beam control system, the feasibility of proposed learning scheme is shown.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.3
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• pp.58-64
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• 1999

Evolutionary programming is mainly characterized by two factors; one is the selection strategy and the other the mutation rule. In this paper, a new mutation rule that is the same form of well-known backpropagation learning rule of neural networks has been presented. The proposed mutation rule adapts the best individual's value as the target value at the generation. The temporal error improves the exploration through guiding the direction of evolution and the momentum speeds up convergence. The efficiency and robustness of the proposed algorithm have been verified through benchmark test functions.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.9
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• pp.1541-1550
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• 2016

In this paper, we introduce a novel learning methodology of fuzzy clustering-based neural network pattern classifier. Fuzzy clustering-based neural network pattern classifier depicts the patterns of given classes using fuzzy rules and categorizes the patterns on unseen data through fuzzy rules. Least squares estimator(LSE) or weighted least squares estimator(WLSE) is typically used in order to estimate the coefficients of polynomial function, but this study proposes a novel coefficient estimate method which includes advantages of the existing methods. The premise part of fuzzy rule depicts input space as "If" clause of fuzzy rule through fuzzy c-means(FCM) clustering, while the consequent part of fuzzy rule denotes output space through polynomial function such as linear, quadratic and their coefficients are estimated by the proposed local least squares estimator(LLSE)-based learning. In order to evaluate the performance of the proposed pattern classifier, the variety of machine learning data sets are exploited in experiments and through the comparative analysis of performance, it provides that the proposed LLSE-based learning method is preferable when compared with the other learning methods conventionally used in previous literature.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.109-112
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• 2006

본 논문은 클래스들의 대표값들과 입력 벡터와의 거리를 사용한 새로운 퍼지 학습법칙을 제안한다. 이 새로운 퍼지 학습을 supervised IAFC(Integrated Adaptive Fuzzy Clustering) 신경회로망에 적용하였다. 이 새로운 신경회로망은 안정성을 유지하면서도 유연성을 가지고 있다. iris 데이터를 사용하여 테스트한 결과 supervised IAFC 신경회로망 4는 오류 역전파 신경회로망과 LVQ 알고리즘보다 성능이 우수하였다.

• Proceedings of the Optical Society of Korea Conference
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• 1989.02a
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• pp.103-106
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• 1989

A method of adaptive formation of the synthetic discriminant function(SDF) both in image plane and spatial frequency plane by using the Widrow-Hoff learning rule is proposed. The proposed method uses minimum number of interconnections between neurons so it can reduce the time for learning the neural net. Also complex valued interconnection weights are introduced for the purposes of handling the phase objects or Fourier transformed spatial frequency objects which usually have complex values for the representation of not only amplitude but also phase information. Also methods of optical implementation for the complex valued interconnection weights are discussed.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1254-1259
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• 2005

In this paper, we proposed a learning algorithm for the neuro-fuzzy modeling using a learning rule to adapt clustering. The proposed algorithm includes the data partition, assigning the rule into the process of partition, and optimizing the parameters using predetermined threshold value in self-constructing algorithm. In order to improve the clustering, the learning method of neuro-fuzzy model is extended and the learning scheme has been modified such that the learning of overall model is extended based on the error-derivative learning. The effect of the proposed method is presented using simulation compare with previous ones.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.95-101
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• 2005

In this paper, we proposed a learning algorithm for the neuro-fuzzy modeling using a learning rule to adapt clustering. The proposed algorithm includes the data partition, assigning the rule into the process of partition, and optimizing the parameters using predetermined threshold value in self-constructing algorithm. In order to improve the clustering, the learning method of neuro-fuzzy model is extended and the learning scheme has been modified such that the learning of overall model is extended based on the error-derivative learning. The effect of the proposed method is presented using simulation compare with previous ones.

• ETRI Journal
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• v.38 no.6
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• pp.1207-1217
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• 2016

The general method of machine learning has encountered disadvantages in terms of the significant amount of time and effort required for feature extraction and engineering in natural language processing. However, in recent years, these disadvantages have been solved using deep learning. In this paper, we propose a mention pair (MP) model using deep learning, and a system that combines both rule-based and deep learning-based systems using a guided MP as a coreference resolution, which is an information extraction technique. Our experiment results confirm that the proposed deep-learning based coreference resolution system achieves a better level of performance than rule- and statistics-based systems applied separately