• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.3
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• pp.88-98
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• 2002

The multilayer perceptron (MLP) has several advantages against other pattern recognition methods, and is expected to be used as the learning and recognizing speakers of speaker verification system. But because of the low learning speed of the error backpropagation (EBP) algorithm that is used for the MLP learning, the MLP learning requires considerable time. Because the speaker verification system must provide verification services just after a speaker's enrollment, it is required to solve the problem. So, this paper tries to make short of time required to enroll speakers with the MLP based speaker verification system, using the method of improving the EBP learning speed and the method of reducing background speakers which adopts the cohort speakers method from the existing speaker verification.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.39
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• pp.47-61
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• 1996

We propose a parallel neural network model in which patterns are clustered and patterns in a cluster are studied in a parallel neural network. The learning algorithm used in this paper is based on LVQ algorithm of Kohonen(1990) for clustering and ADALINE(Adaptive Linear Neuron) network of Widrow and Hoff(1990) for parallel learning. The proposed algorithm consists of two parts. First, N patterns to be learned are categorized into C clusters by LVQ clustering algorithm. Second, C patterns that was selected from each cluster of C are learned as input pattern of ADALINE(Adaptive Linear Neuron). Data used in this paper consists of 250 patterns of ASCII characters normalized into $8\times16$ and 1124. The proposed algorithm consists of two parts. First, N patterns to be learned are categorized into C clusters by LVQ clustering algorithm. Second, C patterns that was selected from each cluster of C are learned as input pattern of ADALINE(Adaptive Linear Neuron). Data used in this paper consists 250 patterns of ASCII characters normalized into $8\times16$ and 1124 samples acquired from signals generated from 9 car models that passed Inductive Loop Detector(ILD) at 10 points. In ASCII character experiment, 191(179) out of 250 patterns are recognized with 3%(5%) noise and with 1124 car model data. 807 car models were recognized showing 71.8% recognition ratio. This result is 10.2% improvement over backpropagation algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.625-628
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• 1997

In this paper the new neural network and sliding mode suspension controller is proposed. That neural network is error self-recurrent neural network. For fast on-line learning, this paper use recursive least squares method. A new neural networks converges considerably faster than the backpropagation algorithm and has advantages of being less affected by the poor initial weights and learning rate. The controller for suspension systems is designed according to sliding mode technique based on new proposed neural network.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1275-1278
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• 2003

본 논문에서는 오차 역전파 알고리즘의 전역 최소값을 찾지 못하는 문제점에 대해서 설명하였고, 이 문제를 해결하기 위한 방법으로 유전자 알고리즘에 대해서 설명하였다. 오차 역전파 알고리즘은 기본적으로 경도 하강법을 따른다. 따라서 신경망의 각 가중값 행렬이 만드는 고차의 오차 평면이 대부분의 문제에서 다수의 국부 최소값들을 가지는게 일반적인데, 가중값의 변화가 한방으로 진행하기 시작하여, 오차가 증가되어지는 언덕이 학습 계수보다 크다면 더 이상 학습은 진행되지 않고 거기에서 빠져나가지 못한다. 따라서 초기의 위치가 중요한 역할을 하는데, 이 문제를 해결하기 위해서 유전자 알고리즘을 이용한 신경망 초기화 방법을 제안하였다. 끝으로, 간단한 실험으로 제안된 방법을 구현하고 결과에 대해서 논하였다

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.745-748
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• 1996

Korea Composite Stock Price Index (KOSPI) was predicted using Cascade Correlation Network (CCN) model. CCN was suggested, by Fahlman and Lebiere [1990], to overcome the limitations of backpropagation algorithm such as step size problem and moving target problem. To test the applicability of CCN as a function approximator to the stock price movements, CCN was used as a tool for univariate time series analysis. The fitting and forecasting performance fo CCN on the KOSPI was compared with those of Multi-Layer Perceptron (MLP).

• Proceedings of the KAIS Fall Conference
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• 2007.05a
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• pp.147-149
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• 2007

"Dynamic Neural Unit"(DNU) based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our methodis different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its trainin.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 1999.11a
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• pp.132-135
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• 1999

In this work, two approaches were proposed for the recognition of partial discharge patterns. The first approach was neural network with backpropagation algorithm, and the second approach was angle calculation between two operator vectors. PD signal were detected using three electrode systems; IEC(b), needle-plane and CIGRE method II electrode system. Both of neural network and angle comparison method showed good recognition performance for the patte군 similar to the trained patterns. And the number of operators to be used had a great influence on the recognition performance to the untrained patterns.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.206-211
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• 1996

A major problem in automation of turning operation is the difficulty in obtaining a sufficient and reliable chip control. Therefore it becomes desirable to find a method which can detect the chip form. In this paper, a method of the identification of chip form using output of pyrometer and neural network technique is developed. An efficiency of developed method is examined by experiments in turning and the validity of it is confirmed.

• Proceedings of the KAIS Fall Conference
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• 2011.05a
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• pp.379-381
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• 2011

Dynamic Neural Unit(DNU) based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our methodis different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its trainin.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.313-315
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• 2001

본 논문에서 소개하는 알고리즘을 은닉층의 뉴런의 수를 학습하는 동안 동적으로 변화시켜 역전파 알고리즘의 단점인 Local maxima를 탈출하고 또한 은닉층의 뉴런의 수를 결정하는 과정을 없애기 위해 연구되었다. 본 알고리즘의 성능을 평가하기 위해 두 가지 실험에 적용하였는데 첫번째는 Exclusive-OR 문제이고 두번째는 7$\times$8 한글 자음과 모음의 폰트 학습에 적용하였다. 이 실험의 결과로 네트웍이 local maxima에 빠져드는 확률이 줄어드는 것을 알 수 있었고 학습속도 또한 일반적인 역전파 알고리즘보다 빠른 것으로 증명되었다.