• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.3
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• pp.140-148
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• 2014

In this paper, We proposed Gaussian function as a basis of hybrid method. Hybrid method is to extrapolate late time and high frequency data using early time and low frequency data. This method takes advantages of both MOT and MOM as well as having shorter running time and smaller error. For this method a better basis function is required. We compared the performance of the result with proposed function and conventional basis including Hermite and Laguerre polynomial.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.26-29
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• 2002

Radial Basis Function (RBF) networks is known as efficient method in classification problems and function approximation. The basis function of RBF networks is usual adopted normal distribution like the Gaussian function. The output of the Gaussian function has the maximum at the center and decrease as increase the distance from the center. For learning of neural network, the method treating the limited area of input space is sometimes more useful than the method treating the whole of input space. The q-normal distribution is the set of probability density function include the Gaussian function. In this paper, we introduce the RBF networks with the basis function of q-normal distribution and actually approximate a function using the RBF networks.

• Journal for History of Mathematics
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• v.35 no.1
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• pp.1-18
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• 2022

This paper presents a statistical machine learning method that generates the implied volatility surface under the rareness of the market data. We apply the practitioner's Black-Scholes model and Gaussian process regression method to construct a Bayesian inference system with observed volatilities as a prior information and estimate the posterior distribution of the unobserved volatilities. The variance instead of the volatility is the target of the estimation, and the radial basis function is applied to the mean and kernel function of the Gaussian process regression. We present two types of Gaussian process regression methods and empirically analyze them.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.16-16
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• 2000

This paper describes a new structure re create a pseudo Gaussian function network (PGFN). The activation function of hidden layer does not necessarily have to be symmetric with respect to center. To give the flexibility of the network, the deviation of pseudo Gaussian function is changed according to a direction of given input. This property helps that given function can be described effectively with a minimum number of center by PGFN, The distribution of deviation is represented by level set method and also the loaming of deviation is adjusted based on it. To demonstrate the performance of the proposed network, general problem of function estimation is treated here. The representation problem of continuous functions defined over two-dimensional input space is solved.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.11
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• pp.2158-2165
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• 2007

In this paper, a Modified Fuzzy C-Means algorithm with Gaussian Weights(MFCM_GW) is presented for nonlinear blind channel equalization. The proposed algorithm searches the optimal channel output states of a nonlinear channel from the received symbols, based on the Bayesian likelihood fitness function and Gaussian weighted partition matrix instead of a conventional Euclidean distance measure. Next, the desired channel states of a nonlinear channel are constructed with the elements of estimated channel output states, and placed at the center of a Radial Basis Function(RBF) equalizer to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with those of a simplex genetic algorithm(GA), a hybrid genetic algorithm(GA merged with simulated annealing(SA): GASA), and a previously developed version of MFCM. It is shown that a relatively high accuracy and fast search speed has been achieved.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.315-315
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• 2000

In this paper, we perform the time series prediction using the SVM(Support Vector Machine). We make use of two different loss functions and two different kernel functions; i) Quadratic and $\varepsilon$-insensitive loss function are used; ii) GRBF(Gaussian Radial Basis Function) and ERBF(Exponential Radial Basis Function) are used. Mackey-Glass time series are used for prediction. For both cases, we compare the results by the SVM to those by ANN(Artificial Neural Network) and show the better performance by SVM than that by ANN.

• The KIPS Transactions:PartB
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• v.11B no.3
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• pp.303-308
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• 2004

The Gabor cosine and sine transform can be applied to image and video compression algorithm by representing image frequency components locally The computational complexity of forward and inverse matrix transforms used in the compression and decompression requires O($N^3$)operations. In this paper, the length of basis functions is truncated to produce a sparse basis matrix, and the computational burden of transforms reduces to deal with image compression and reconstruction in a real-time processing. As the length of basis functions is decreased, the truncation effects to the energy of basis functions are examined and the change in various Qualify measures is evaluated. Experiment results show that 11 times fewer multiplication/addition operations are achieved with less than 1% performance change.

• Journal of applied mathematics & informatics
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• v.27 no.5_6
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• pp.1087-1095
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• 2009

In this paper, we study approximation method from scattered data to the derivatives of a function f by a radial basis function $\phi$. For a given function f, we define a nearly interpolating function and discuss its accuracy. In particular, we are interested in using smooth functions $\phi$ which are (conditionally) positive definite. We estimate accuracy of approximation for the Sobolev space while the classical radial basis function interpolation applies to the so-called native space. We observe that our approximant provides spectral convergence order, as the density of the given data is getting smaller.

• The Journal of the Acoustical Society of Korea
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• v.18 no.7
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• pp.112-117
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• 1999

In case of spectral subtraction for noise robust speech recognition system, this method often makes loss of speech signal. In this study, we propose a method that variation and determination of Gaussian function at semi-continuous HMM(Hidden Markov Model) is made on the basis of SNR criteria function, in which SNR means signal to noise ratio between estimation noise and subtracted signal per frame. For proving effectiveness of this method, we show the estimation error to be related with the magnitude of estimated noise through signal waveform. For this reason, Gaussian function is varied and determined by SNR. When we test recognition rate by computer simulation under the noise environment of driving car over the speed of 80㎞/h, the proposed Gaussian decision method by SNR turns out to get more improved recognition rate compared with the frequency subtracted and non-subtracted cases.

• Science of Emotion and Sensibility
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• v.17 no.2
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• pp.63-76
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• 2014

Although most behavioral reaction times (RTs) for cognitive tasks exhibit positively skewed distributions, the majority of studies primarily rely on a measure of central tendency (e.g. mean) which can cause misinterpretations of data's underlying property. The purpose of current study is to introduce procedures for describing characteristics of RT distributions, thereby effectively examine the influence of experimental manipulations. On the basis of assumption that RT distribution can be represented as a convolution of Gaussian and exponential variables, we fitted the ex-Gaussian function under a maximum-likelihood method. The ex-Gaussian function provides quantitative parameters of distributional properties and the probability density functions. Here we exemplified distributional analysis by using empirical RT data from two conventional visual search tasks, and attempted theoretical interpretation for setsize effect leading proportional mean RT delays. We believe that distributional RT analysis with a mathematical function beyond the central tendency estimates could provide insights into various theoretical and individual difference studies.