• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.297-300
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• 2001

Artificial neural networks have become important tools in many fields. This paper describes a new algorithm fur training an RBF network. This algorithm has two main advantages: higher accuracy and a too stable learning process. In addition, it can be used as a good classifier in pattern recognition.

• Proceedings of the SAREK Conference
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• 2004.06a
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• pp.144-144
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• 2004

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.140-145
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• 2010

In this work, to theoretically analyze the nonlinear charging characteristic, a Radial Basis Function Neural Network (RBFNN) is adopted. Based on the RBFNN, an charging characteristic tendency of a Linear Type Magnetic Flux Pump (LTMFP) is analyzed. In the paper, we developed the LTMFP that generates stable and controllable charging current and also experimentally investigated its charging characteristic in the cryogenic system. From these experimental results, the charging current of the LTMFP was also found to be frequency dependent with nonlinear quality due to the nonlinear magnetic behaviour of superconducting Nb foil. On the whole, in the case of essentially cryogenic experiment, since cooling costs loomed large in the cryogenic environment, it is difficult to carry out various experiments. Consequentially, in this paper, we estimated the nonlinear characteristic of charging current as well as realized the intelligent model via the design of RBFNN based on the experimental data. In this paper, we view RBF neural networks as predominantly data driven constructs whose processing is based upon an effective usage of experimental data through a prudent process of Fuzzy C-Means clustering method. Also, the receptive fields of the proposed RBF neural network are formed by the FCM clustering.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.30-33
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• 2002

To obtain good performance of radial basis function (RBF) neural networks, it needs very careful consideration in design. The selection of several parameters such as the number of centers and widths of the radial basis functions must be considered carefully since they critically affect the network's performance. We propose a learning algorithm for growing of complexity of RBF neural networks which is adapted automatically according to the complexity of tasks. The algorithm generates a new basis function based on the errors of network, the percentage of decreasing rate of errors and the nearest distance from input data to the center of hidden unit. The RBF's center is located at the point where the maximum of absolute interference error occurs in the input space. The width is calculated based on the standard deviation of distance between the center and inputs data. The steepest descent method is also applied for adjusting the weights, centers, and widths. To demonstrate the performance of the proposed algorithm, general problem of function estimation is evaluated. The results obtained from the simulation show that the proposed algorithm for RBF neural networks yields good performance in terms of convergence and accuracy compared with those obtained by conventional multilayer feedforward networks.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.715-718
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• 2006

아바타를 이용한 얼굴 애니메이션은 가상 현실이나 엔터테인먼트와 같은 분야에서 많이 적용된다. 얼굴 애니메이션을 생성하는 방법에는 크게 3 차원 모델을 직접 변형시키는 기하학적인 변형 방법과 2 차원 이미지의 워핑이나 모핑방법을 이용한 이미지 변형 방법이 있다. 기하학적인 변형 방법 중 3 차원 모델을 변형시키기 위한 방법으로 RBF(Radial Basis Function)을 이용하는 방법이 있다. RBF 함수를 이용하여 모델의 부드러운 변형을 만들 수 있다. 이 방법은 모델의 임의의 한 점을 이동하게 되면 영향을 받는 정점들을 좀 더 자연스럽게 이동시킴으로써 자연스러운 애니메이션을 생성할 수 있다. 본 연구에서는 RBF 를 이용하여 3 차원 얼굴 메쉬 모델의 기하학적 변형을 통해 모델의 얼굴 표정을 생성하는 방법에 대해 제안하고자 한다. 얼굴 모델 변형을 위해 얼굴의 특징인 눈, 입, 턱 부분에 특징점을 정하고 각 특징점에 따라 영향을 받는 영역을 정하기 위해 얼굴 모델을 지역적으로 클러스터링한다. 각 특징점에 따라 영향을 받는 영역에 대해 클러스터링을 적용하고 RBF 를 이용하여 자연스러운 얼굴 표정을 생성하는 방법을 제안한다.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.4
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• pp.364-371
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• 2013

Emotion Recognition is one of the important part to develop in human-human and human computer interaction. In this paper, we have focused on the performance of multi-class SVM (Support Vector Machine) with Gaussian RFB (Radial Basis function) kernel, which has been used to solve the problem of emotion recognition from physiological signals and to improve the accuracy of emotion recognition. The experimental paradigm for data acquisition, visual-stimuli of IAPS (International Affective Picture System) are used to induce emotional states, such as fear, disgust, joy, and neutral for each subject. The raw signals of acquisited data are splitted in the trial from each session to pre-process the data. The mean value and standard deviation are employed to extract the data for feature extraction and preparing in the next step of classification. The experimental results are proving that the proposed approach of multi-class SVM with Gaussian RBF kernel with OVO (One-Versus-One) method provided the successful performance, accuracies of classification, which has been performed over these four emotions.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.222-227
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• 2004

Today, an automatic and accurate processing using computer is essential because of the rapid increase of travelers. The determination of forged passports plays an important role in the immigration control system. Hence, as the preprocessing phase for the determination of forged passports, this paper proposes a novel method for the recognition of passports based on the fuzzy binarization and the fuzzy RBF network. First, for the extraction of individual codes for recognizing, this paper targets code sequence blocks including individual codes by applying Sobel masking, horizontal smearing and a contour tracking algorithm on the passport image. Then the proposed method binarizes the extracted blocks using fuzzy binarization based on the trapezoid type membership function. Then, as the last step, individual codes are recovered and extracted from the binarized areas by applying CDM masking and vertical smearing. This paper also proposes an enhanced fuzzy RBF network that adapts the enhanced fuzzy ART network for the middle layer. This network is applied to the recognition of individual codes. The results of the experiments for performance evaluation on the real passport images showed that the proposed method has the better performance compared with other approaches.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.5
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• pp.526-531
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• 1997

To control nonlinear chemical processes, a new neural linearizing control scheme is proposed. This is a hybrid of a radial basis function(RBF) network and a linear controller, thus the control action applied to the process is the sum of both control actions. Firstly, to train the RBF newtork a linear reference model is determined by analyzing the past operating data of the process. Then, the training of the RBF newtork is iteratively performed to minimize the difference between outputs of the process and the linear reference model. As a result, the apparent dynamics of the process added by the RBF newtork becomes similar to that of the linear reference model. After training, the original nonlinear control problem changes to a linear one, and the closed-loop control performance is improved by using the optimum tuning parameters of the linear controller for the linear dynamics. The proposed control scheme performs control and training simultaneously, and shows a good control performance for nonlinear chemical processes.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.11
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• pp.89-98
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• 1998

The enhanced resource-allocating network(ERAN) that adaptively generates hidden units of radial basis function(RBF) network for systems modeling has been proposed. The ERAN is an improved version of the resource-allocating network(RAN) that allocates new hidden units based on the novelty of observation data. The learning process of the ERAN involves allocation of new hidden units and adjusting the network parameters. The network starts with no hidden units. As observation data are received, the network adds a hidden units only if the three network growth criteria are satisfied. The network parameters are adjusted by the LMS algorithm. The performance of the ERAN is compared with the RAN for nonlinear static systems modeling problem with sequential and random learning. For two simulations, the ERAN has been shown to realize RBF networks with better accuracy with fewer hidden units.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.9
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• pp.103-110
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• 2010

This paper presents the optimized approximation of finite element modeling for a complex tool holder spindle using both DOE (Design of Experiment) with Optimal Latin Hypercube (OLH) method and approximation modeling method with Radial Basis Function (RBF) neural network structure. The complex tool holder is used for holding a (milling/drilling) tool of a machine tool. The engineering problem of complex tool holder results from the twisting of spindle of tool holder. For this purpose, we present the optimized approximation of finite element modeling for a complex tool holder spindle using both DOE (Design of Experiment) with Optimal Latin Hypercube (OLH) method (specifically a module of $iSight^{(R)}$ FD-3.1) and approximation modeling method with Radial Basis Function (RBF) (another module of $iSight^{(R)}$ FD-3.1) neural network structure