• 대한전기학회논문지
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• 제40권2호
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• pp.224-233
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• 1991

The multilayer expanson of single layer NN (Neural Network) was needed to solve the linear seperability problem as shown by the classic example using the XOR function. The EBP (Error Back Propagation ) learning rule is often used in multilayer Neural Networks, but it is not without its faults: 1)D.Rimmelhart expanded the Delta Rule but there is a problem in obtaining Ca from the linear combination of the Weight matrix N between the hidden layer and the output layer and H, wich is the result of another linear combination between the input pattern and the Weight matrix M between the input layer and the hidden layer. 2) Even if using the difference between Ca and Da to adjust the values of the Weight matrix N between the hidden layer and the output layer may be valid is correct, but using the same value to adjust the Weight matrixd M between the input layer and the hidden layer is wrong. Recognitron III was proposed to solve these faults. According to simulation results, since Recognitron III does not learn the three layer NN itself, but divides it into several single layer NNs and learns these with learning patterns, the learning time is 32.5 to 72.2 time faster than EBP NN one. The number of patterns learned in a EBP NN with n input and output cells and n+1 hidden cells are 2**n, but n in Recognitron III of the same size. [5] In the case of pattern generalization, however, EBP NN is less than Recognitron III.

• 한국지능시스템학회논문지
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• 제14권7호
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• pp.830-837
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• 2004

본 논문에서는 비선형시스템에서 발생한 고장을 감지하고 분류하기 위한 신경회로망기반 고장진단 방법을 제안한다. 제안한 알고리듬에서는 시스템의 출력과 다층신경회로망 공칭모델 출력 사이의 오차가 미리 설정한 문턱값을 넘으면 고장을 감지한다. 고장이 감지되면 다층신경회로망과 ART2 신경회로망을 이용한 고장분류기에서 시스템에서 발생한 고장을 분류한다. 컴퓨터 시뮬레이션 결과로부터 제안한 고장진단방법이 비선형시스템에서의 고장감지 및 분류문제에 잘 적용됨을 알 수 있다.

• Computers and Concrete
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• 제12권2호
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• pp.229-241
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• 2013

The objective of this study is to reveal the sufficiency of neural networks (NN) as a securer, quicker, more robust and reliable method to be used in seismic assessment of existing reinforced concrete buildings. The NN based approach is applied as an alternative method to determine the seismic performance of each existing RC buildings, in terms of damage level. In the application of the NN, a multilayer perceptron (MLP) with a back-propagation (BP) algorithm is employed using a scaled conjugate gradient. NN based model wasd eveloped, trained and tested through a based MATLAB program. The database of this model was developed by using a statistical procedure called P25 method. The NN based model was also proved by verification set constituting of real existing RC buildings exposed to 2003 Bingol earthquake. It is demonstrated that the NN based approach is highly successful and can be used as an alternative method to determine the seismic performance of each existing RC buildings.

• 대한산업공학회지
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• 제25권1호
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• pp.111-124
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• 1999

This evapaper is toluate the forecasting performance of three neural network(NN) approaches against ARIMA model using the famous time series analysis competition data. The first NN approach is to analyze the second Makridakis (M2) Competition Data using Multilayer Perceptron (MLP) that has been the most popular NN model in time series analysis. Since it is recently known that MLP suffers from bias/variance dilemma, two approaches are suggested in this study. The second approach adopts Cascade Correlation Network (CCN) that was suggested by Fahlman & Lebiere as an alternative to MLP. In the third approach, a time series is separated into two series using Noise Filtering Network (NFN) that utilizes autoassociative memory function of neural network. The forecasts in the decomposition analysis are the sum of two prediction values obtained from modeling each decomposed series, respectively. Among the three NN approaches, Decomposition Analysis shows the best forecasting performance on the M2 Competition Data, and is expected to be a promising tool in analyzing socio-economic time series data because it reduces the effect of noise or outliers that is an impediment to modeling the time series generating process.

• 한국컴퓨터산업교육학회:학술대회논문집
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• 한국컴퓨터산업교육학회 2003년도 제4회 종합학술대회 논문집
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• pp.59-64
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• 2003

Principal component analysis(PCA) and neural network(NN) are used in reducing external noise in magnetocadiography. The PCA technique turns out to be very effective in reducing pulse noise in some SQUID channels and the NN find noise component automatically. Some experimental results obtained from 61 channel MCG system are shown.

• 전력전자학회:학술대회논문집
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• 전력전자학회 1999년도 전력전자학술대회 논문집
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• pp.526-530
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• 1999

This paper presents a newly developed speed sensorless drive using Neural Network algorithm. Neural Network algorithm can be divided into three categories. In the first one, a Back Propagation-based NN algorithm is well-known to gradient descent method. In the second scheme, a Extended Kalman Filter-based NN algorithm has just the time varying learning rate. In the last scheme, a Recursive Least Square-based NN algorithm is faster and more stable than the classical back-propagation algorithm for training multilayer perceptrons. The number of iterations required to converge and the mean-squared error between the desired and actual outputs is compared with respect to each method. The theoretical analysis and experimental results are discussed.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 추계학술대회 논문집 학회본부
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• pp.58-61
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• 1996

For a class of single-input single-output continuous-time nonlinear systems, a multilayer neural network-based controller that feedback-linearizes the system is presented. Control action is used to achieve tracking performance for a state-feedback linearizable but unknown nonlinear system. The multilayer neural network(NN) is used to approximate nonlinear continuous function to any desired degree of accuracy. The weight-update rule of multilayer neural network is derived to satisfy Lyapunov stability. It is shown that all the signals in the closed-loop system are uniformly bounded. Initialization of the network weights is straightforward.

• Geomechanics and Engineering
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• 제33권2호
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• pp.183-194
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• 2023

This study presents the prediction of the California bearing ratio (CBR) of coarse- and fine-grained soils using artificial intelligence technology. The group method of data handling (GMDH) algorithm, an artificial neural network-based model, was used in the prediction of the CBR values. In the design of the prediction models, various combinations of independent input variables for both coarse- and fine-grained soils have been used. The results obtained from the designed GMDH-type neural networks (GMDH-type NN) were compared with other regression models, such as linear, support vector, and multilayer perception regression methods. The performance of models was evaluated with a regression coefficient (R²), root-mean-square error (RMSE), and mean absolute error (MAE). The results showed that GMDH-type NN algorithm had higher performance than other regression methods in the prediction of CBR value for coarse- and fine-grained soils. The GMDH model had an R² of 0.938, RMSE of 1.87, and MAE of 1.48 for the input variables {G, S, and MDD} in coarse-grained soils. For fine-grained soils, it had an R² of 0.829, RMSE of 3.02, and MAE of 2.40, when using the input variables {LL, PI, MDD, and OMC}. The performance evaluations revealed that the GMDH-type NN models were effective in predicting CBR values of both coarse- and fine-grained soils.

• 한국콘텐츠학회논문지
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• 제10권4호
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• pp.84-90
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• 2010

데이터베이스 시스템의 응용분야가 데이터웨어하우징에서 전자상거래에 이르기까지 광범위해지면서 데이터베이스 시스템이 대형화되었다. 이로 인해 데이터베이스 시스템의 성능 향상을 위한 튜닝이 중요한 논점이 되었다. 데이터베이스 시스템의 튜닝은 워크로드 특성을 고려하여 수행할 필요가 있다. 그러나 복합적인 데이터베이스 환경에서 워크로드를 식별하기는 어려우므로 자동적인 식별 방법이 요구된다. 본 논문에서는 데이터베이스 워크로드를 자동적으로 식별하는 SVM 워크로드 분류기를 제안한다. TPC-C와 TPC-W 성능 평가에서 자원할당 파라미터 변경에 따른 워크로드 데이터를 수집하여 SVM을 통해 분류 한다. SVM의 커널별 커널 파라미터와 오류 허용 임계치 값인 C의 조정을 통하여 최적의 SVM 워크로드 분류기를 선택한다. 제안한 SVM 워크로드 분류기와 Decision Tree, Naive Bayes, Multilayer Perceptron, K-NN 분류기의 분류 성능을 비교한 결과, SVM 워크로드 분류기가 다른 기계 학습 분류기보다 9% 이상 향상된 분류 성능을 보였다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2015년도 추계학술발표대회
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• pp.1361-1364
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• 2015

본 연구에서는 노인들이 일상에서 많이 행동하는 7가지 유형의 행동의 특징을 추출하고, 총 7가지 분류 알고리즘에 적용하여 가장 인식률이 높은 알고리즘을 도출하고자 하였다. 행동패턴은 정상보행, 절름발이, 지팡이, 느린 보행, 허리가 굽은 상태에서 보행, 스스로 휠체어 끌 때 그리고 누군가가 휠체어를 끌어줄 때 총 7가지로 구성하였다. 행동패턴의 특징은 3축 가속도 센서의 값, 평균, 표준편차, 수직 및 수평축의 데이터를 사용하였다. 분류 알고리즘은 Naive Bayes, Bayes Net, k-NN, SVM, Decision Tree, Multilayer perception, Logistic regression을 사용하였다. 연구결과 k-NN 알고리즘의 인식률이 98.7%로 다른 분류알고리즘에 비해 인식률이 높게 나타났다.