• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.5C
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• pp.555-560
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• 2003

For synchronous direct sequence-code division multiple access (DS-CDMA) systems, the authors propose an adaptive radial basis function (RBF) receiver with suboptimal structure that reduces not only the complexity with regard to the number of centers but also the quantity of instructions required per one bit reception. The proposed receiver is constructed with parallel RBF networks. Each RBF network has the same procedure as the conventional RBF receiver. The performance of each RBF network is affected by interferences which are assigned to the other RBF networks because neither RBF network uses the full user set. To combat these interferences, the partial IC technique is employed. Monte Carlo simulations over additive white Gaussian noise (AWGN) channels confirm that the proposed receiver with its reduced complexity is able to obtain near-optimum performance. Moreover, the proposed receiver is able to properly cope with a various environment.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.1
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• pp.48-53
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• 2002

In this paper, we presented a new approach to the recognition of unconstrained handwritten numerals using an improved RBF(Radial Basis Function) Neural Networks. The RBF Neural Networks used Raised Cosine as a basis function to improve discrimination and reduce processing time. The performance of Raised Cosine RBF Neural Networks classifier was evaluated using totally unconstrained handwritten numeral database of Concordia University, Montreal, Canada, and the experimental results showed the recognition rate of 98.05%.

• Journal of Life Science
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• v.8 no.2
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• pp.119-125
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• 1998

Column-purified double-stranded RNA binding factor (RBF) protein was tested for its binding affinity for the different forms of nucleic acids structure such as single-stranded(ss) and double-stranded(ds)RNA and ss- and dsDNA. The RBF protein was incubated with each of these nucleic acid structures in separate reactions and its comparative binding affnity was visualized by SDS-polyacrylamide gel electrophoresis. The RBF protein bound to the dsRNA molecule to form a tight RNA:protein complex in agreement with previous studies, but not to the other nucleic acid molecules confirming its distinctive affinity for the dsRNA structure. In phosphorylation assay in vito, the purified RBF protein significantly inhibited the autophosphorylation of the PKR derived from not only human but mouse source in the presence of poly(I):poly(C). It is suggesting that PKR vs. RBF is similarly under a competitive interaction among different eukaryotic organisms during protein synthesis.

• Journal of Korea Multimedia Society
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• v.9 no.3
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• pp.369-376
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• 2006

In this paper, we propose an improved fuzzy RBF network which dynamically adjusts the rate of learning by applying the Delta-bar-Delta algorithm in order to improve the learning performance of fuzzy RBF networks. The proposed learning algorithm, which combines the fuzzy C-Means algorithm with the generalized delta learning method, improves its learning performance by dynamically adjusting the rate of learning. The adjustment of the learning rate is achieved by self-generating middle-layered nodes and by applying the Delta-bar-Delta algorithm to the generalized delta learning method for the learning of middle and output layers. To evaluate the learning performance of the proposed RBF network, we used 40 identifiers extracted from a container image as the training data. Our experimental results show that the proposed method consumes less training time and improves the convergence of teaming, compared to the conventional ART2-based RBF network and fuzzy RBF network.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.2
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• pp.26-35
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• 1996

In this paper, some fundamental problems concerning RBF(radial-basis-function) networks and approximation of functions are addressed. First, a comprehensive introduction to RBF networks is given with typical RBF networks classified into three classes. Next, sharp conditions are given under which continuous functions of a finite number of real variables can be approximated arbitrarily well by a certain class of RBF networks. Finally, a related result is given concerning the representation of functions in the form of distributed RBF networks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.3
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• pp.525-531
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• 2001

The paper demonstrates that RBF(Radial Basis Function) networks can be used effective for the identification of inverted pendulum system. With the parallel arrangement of the RBF networks controller and PD controller, some characteristics were compared through simulation performance.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.4
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• pp.243-250
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• 2003

In this paper, the speed controller of permanent-magnet synchronous motor (PMSM) using the RBF neural (NN) disturbance observer is proposed. The suggested controller is designed using the input-output feedback linearization technique for the nominal model of PMSM and incorporates the RBF NN disturbance observer to compensate for the system uncertainties. Because the RBF NN disturbance observer which estimates the variation of a system parameter and a load torque is employed, the proposed algorithm is robust against the uncertainties of the system. Finally, the computer simulation is carried out to verify the effectiveness of the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.8C
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• pp.795-800
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• 2006

In this paper, the minimum mean square error(MSE) convergence of the RBF equalizer is evaluated and compared with the linear equalizer based on the theoretical minimum MSE. The basic idea of comparing these two equalizers comes from the fact that the relationship between the hidden and output layers in the RBF equalizer is also linear. As extensive studies of this research, various channel models are selected, which include linearly separable channel, slightly distorted channel, and severely distorted channel models. In this work, the theoretical minimum MSE for both RBF and linear equalizers were computed, compared and the sensitivity of minimum MSE due to RBF center spreads was analyzed. It was found that RBF based equalizer always produced lower minimum MSE than linear equalizer, and that the minimum MSE value of RBF equalizer was obtained with the center spread which is relatively higher(approximately 2 to 10 times more) than variance of AWGN. This work provides an analytical framework for the practical training of RBF equalizer system.

• Journal of the Korean Society of Food Culture
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• v.37 no.6
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• pp.529-539
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• 2022

This study investigated the quality characteristics of madeleines prepared using varying amounts of roasted black soybean flour (RBF). The RBF was used to substitute 0% (control group), 20% (RBF-20 group), 40% (RBF-40 group) and 60% (RBF-60 group) of weak flour (WF) in the manufacture of madeleine. The substitution of WF with RBF showed decreased the pH but increased the sugar concentration of the batter (p<0.01). Low lightness (L) and low yellowness (b) were observed in the experimental groups at high ratios of RBF substitution (p<0.05). The experimental groups of madeleines showed higher hardness and chewiness than the control group (p<0.001). The principal component analysis of the RBF-60 experimental group, which had the highest proportion of RBF, showed that it had relatively strong characteristics with respect to "darkness", "soybean odor", "sesame odor", "grains odor", "savory flavor", "sweetness", "black soybean taste", and "moistness". The acceptance test results, showed that the RBF-20 experimental group was similar to the control group with respect to "odor acceptance", "taste acceptance", and "texture acceptance". Thus, this study confirmed the possibility of using RBF for the preparation of madeleines.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.1
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• pp.35-47
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• 2002

This paper proposes a self organizing RBF neural network equalizer for the equalization of digital communications. It is the most important for the equalizer using the RBF neural network to estimate the RBF centers correctly and quickly, which are the desired channel states. However, the previous RBF equalizers are not used in the actual communication system because of some drawbacks that the number of channel states has to be known in advance and many centers are necessary. Self organizing neural network equalizer proposed in this paper can implement the equalization without prior information regarding the number of channel states because it selects RBF centers among the signals that are transmitted to the equalizer by the new addition and removal criteria. Furthermore, the proposed equalizer has a merit that is able to make a equalization with fewer centers than those of prior one by the course of the training using LMS and clustering algorithm. In the linear, nonlinear and standard telephone channel, the proposed equalizer is compared with the optimal Bayesian equalizer for the BER performance, the symbol decision boundary and the number of centers. As a result of the comparison, we can confirm that the proposed equalizer has almost similar performance with the Bavesian enualizer.