• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.77-87
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• 2021

In this study, a novel improved second order Radial Basis Function Neural Network based method with excellent scheduling capabilities is used for the dynamic prediction of short and long-term energy required applications. The effectiveness and the reliability of the algorithm are evaluated using training operations with New England-ISO database. The dynamic prediction algorithm is implemented in Matlab and the computation of mean absolute error and mean absolute percent error, and training time for the forecasted load, are determined. The results show the impact of temperature and other input parameters on the accuracy of solar Photovoltaic load forecasting. The mean absolute percent error is found to be between 1% to 3% and the training time is evaluated from 3s to 10s. The results are also compared with the previous studies, which show that this new method predicts short and long-term load better than sigmoidal neural network and bagged regression trees. The forecasted energy is found to be the nearest to the correct values as given by England ISO database, which shows that the method can be used reliably for short and long-term load forecasting of any electrical system.

• 한국정보전자통신기술학회논문지
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• 제1권3호
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• pp.69-76
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• 2008

본 논문에서는 Mountain clustering 알고리즘을 이용한 Fuzzy Radial Basis Function Neural Network(FRBFNN)의 규칙 수를 자동생성 방법을 제시한다. FRBFNN은 기존 RBFNN에서 가우시안이나 타원형 형태의 특정 RBF를 사용하는 구조와 달리 클러스터의 중심값과의 거리에 기반을 둔 멤버쉽함수를 사용하여 전반부의 공간 분할 및 활성화 레벨을 결정한다. 또한 분할된 로컬영역에서의 입출력 특성을 나타내는 퍼지규칙의 후반부로서 고차 다항식을 고려하였다. 본 논문에서는 데이터의 밀집도에 기반을 두어 클러스터링을 수행하는 Mountain clustering 알고리즘을 사용하여 적합한 퍼지 규칙(클러스터)의 수와 클러스터의 중심값을 자동적으로 생성하는 방법을 제안한다. Mountain clustering으로부터 구해진 클러스터의 중심은 멤버쉽 값을 결정하는데 사용되며, Weighted Least Square Estimator (WLSE) 알고리즘을 사용하여 후반부 다항식의 계수를 추정한다. 제안된 알고리즘은 비선형 함수 모델링에 적용하여 성능의 우수성과 알고리즘의 타당성을 보인다.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2003년도 추계학술대회
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• pp.368-373
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• 2003

A major concern at present is the simultaneous control of transverse thickness profile and flatness in the finishing stages of hot rolling process. The mathematical modeling of hot rolling process has long been recognized to be a desirable approach to investigate rolling operating practice and the design of mill equipment to improve productivity and quality. However, many factors make the mathematical analysis of the rolling process very complex and time-consuming. In order to overcome these problems and to obtain an accurate rolling force, the predicted model of rolling force using neural networks has widely been employed. In this paper, Radial Basis Function Network(RBFN) is applied to improve the accuracy of rolling force prediction in hot rolling mill. In order to verify and analysis the performance of applied neural network, the comparison with the measured rolling force and the predicted results using two different neural networks - RBFN, MLP, has respectively been carried out. The results obtained using RBFN neural network are much more accurate those obtained the MLP.

• 한국공작기계학회논문집
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• 제13권6호
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• pp.29-33
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• 2004

A major concern at present is the simultaneous control of transverse thickness profile and flatness in the finishing stages of hot rolling process. The mathematical modeling of hot rolling process has long been recognized to be a desirable approach to investigate rolling operating practice and the design of mill equipment to improve productivity and quality. However, many factors make the mathematical analysis of the rolling process very complex and time-consuming. In order to overcome these problems and to obtain an accurate rolling force, the predicted model of rolling force using neural networks has widely been employed. In this paper, Radial Basis Function Network(RBFN) is applied to improve the accuracy of rolling force prediction in hot rolling mill. In order to verify and analyze the performance of applied neural network the comparison with the measured rolling force and the predicted results using two different neural networks-RBFN, MLP, has respectively been carried out. The results obtained using RBFN neural network are much more accurate those obtained the MLP.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1326-1328
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• 2003

An elliptical basis function (EBF) network is proposed in this study for the classification of remotely sensed images. Though similar in structure, the EBF network differs from the well-known radial basis function (RBF) network by incorporating full covariance matrices and uses the expectation-maximization (EM) algorithm to estimate the basis functions. Since remotely sensed data often take on mixture -density distributions in the feature space, the proposed network not only possesses the advantage of the RBF mechanism but also utilizes the EM algorithm to compute the maximum likelihood estimates of the mean vectors and covariance matrices of a Gaussian mixture distribution in the training phase. Experimental results show that the EM-based EBF network is faster in training, more accurate, and simpler in structure.

• Journal of Communications and Networks
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• 제12권6호
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• pp.600-604
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• 2010

A method of width adaptation in the radial basis function network (RBFN) using stochastic gradient (SG) algorithm is introduced. Using Taylor's expansion of error signal and differentiating the error with respect to the step-size, the optimal time-varying step-size of the width in RBFN is derived. The proposed approach to adjusting widths in RBFN achieves superior learning speed and the steady-state mean square error (MSE) performance in nonlinear channel environment. The proposed method has shown enhanced steady-state MSE performance by more than 3 dB in both nonlinear channel environments. The results confirm that controlling over step-size of the width in RBFN by the proposed algorithm can be an effective approach to enhancement of convergence speed and the steady-state value of MSE.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권2호
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• pp.116-123
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• 2013

Currently, most automobiles have automatic transmission systems. The gear-shifting strategy used to generate shift patterns in transmission systems plays an important role in improving the performance of vehicles. However, conventional transmission systems have a fixed type of shift map, so it may not be enough to provide an efficient gear-shifting pattern to satisfy the demands of driver. In this study, we developed an intelligent strategy to handle these problems. This approach is based on a normalized radial basis function neural network, which can generate a flexible gear-shift pattern to satisfy the demands of drivers, including comfortable travel and fuel consumption. The method was verified through simulations.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1995년도 춘계학술대회
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• pp.265-269
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• 1995

In this study, we present a training method of radial basis function networks based on recursive modified Gram-Schmidt algorithm for single step prediction of chaotic time series. With single step predictions of Mackey-Glass time series and alpha-rhythm EEG which has chaotic characteristics, the radial basis function network trained by this method is compared with one trained by a classical non-recursive method and the radial basis function model proposed by X.D. He and A. Lapedes. The results show the effectiveness of the training method.

• 한국통신학회논문지
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• 제28권5C호
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• pp.452-460
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• 2003

본 논문에서는 optimal Bayesian equalization solution인 RBF(radial basis function)를 이용한 등화기 (RE)의 구조를 보다 단순화하고, 비선형 왜곡 등의 심각한 정보 신호의 손상에 대한 보상 능력을 향상시키기 위하여 비선형 다층 결합을 갖는 RBF측 이용한 등화기(RNE)를 새로이 제안한다. 기존의 RE는 RBF로 구성된 은닉층의 출력 값을 선형 결합하여 등화기 출력을 얻는다. 이와 달리 새로이 제안하는 RNE는 기존의 RE에서 RBF로 구성된 은닉층의 출력 값에 대한 결합 기법으로 perceptron을 이용한 비선형 다층 결합을 사용한다. 제안한 equalizer를 결정궤환 방식이 있는 경우와 없는 경우의 등화기로 각각 구현한다. 실험 결과 제안한 등화기는 선형 간섭이 존재하는 디지털 통신 시스템과 비선형 왜곡이 존재하는 자기기록 시스템에서보다 간단한 구조로 기존의 optimal Bayesian 등화기와 거의 같거나 우수한 비트 오류 화률 성능 및 MSE(men squared error) 수렴 특성을 나타내었다.

• Communications for Statistical Applications and Methods
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• 제14권3호
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• pp.601-608
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• 2007

Lee, et al. (1999) proposed a unique but universal robust objective function replacing the square objective function for the radial basis function network, and demonstrated some advantages. In this article, the robust objective function in Lee, et al. (1999) is adapted for a multilayer perceptron (MLP). The shape of the robust objective function is formed by the scale parameter. Another method of determining a proper value of that parameter is proposed.