• 대한기계학회:학술대회논문집
• /
• 대한기계학회 2004년도 춘계학술대회
• /
• pp.1183-1188
• /
• 2004

The inverse heat conduction problem (IHCP) is a problem of estimating boundary condition from temperature measurement at one or more interior points. Neural networks are general information processing systems inspired by the connectionist theory of human brain. By properly training the network by the learning rule, the neural network method can handle many non-linear or other complex problems. In this work, neural network is applied to complicated inverse heat conduction problems. Efficiency of the procedure is enhanced by incorporating the radial basis functions (RBF). The RBF is trained faster than other neural network and can find smooth solution. In order to demonstrate the effectiveness of the current scheme, a typical one-dimensional IHCP is considered. At one surface, the temperature as well as the heat flux is known. The unknown temperature of interest is estimated on the other side of the slab. The results from the proposed method based on RBF neural network are compared with the conventional method.

• Communications for Statistical Applications and Methods
• /
• 제22권4호
• /
• pp.349-359
• /
• 2015

We study a semiparametric Bayesian approach to small area estimation under a nested error linear regression model with area level covariate subject to measurement error. Consideration is given to radial basis functions for the regression spline and knots on a grid of equally spaced sample quantiles of covariate with measurement errors in the nested error linear regression model setup. We conduct a hierarchical Bayesian structural measurement error model for small areas and prove the propriety of the joint posterior based on a given hierarchical Bayesian framework since some priors are defined non-informative improper priors that uses Markov Chain Monte Carlo methods to fit it. Our methodology is illustrated using numerical examples to compare possible models based on model adequacy criteria; in addition, analysis is conducted based on real data.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• 제25권3호
• /
• pp.93-106
• /
• 2021

In this paper, an efficient hybrid numerical method for solving two-asset option pricing problem is presented based on the Crank-Nicolson and the radial basis function methods. For this purpose, the two-asset Black-Scholes partial differential equation is considered. Also, the convergence of the proposed method are proved and implementation of the proposed hybrid method is specifically studied on Exchange and Call on maximum Rainbow options. In addition, this method is compared to the explicit finite difference method as the benchmark and the results show that the proposed method can achieve a noticeably higher accuracy than the benchmark method at a similar computational time. Furthermore, the stability of the proposed hybrid method is numerically proved by considering the effect of the time step size to the computational accuracy in solving these problems.

• 전기학회논문지
• /
• 제66권7호
• /
• pp.1131-1140
• /
• 2017

This study is concerned with a design methodology of optimized fuzzy clustering-based neural network classifier for classifying black plastic. Since the amount of waste plastic is increased every year, the technique for recycling waste plastic is getting more attention. The proposed classifier is on a basis of architecture of radial basis function neural network. The hidden layer of the proposed classifier is composed to FCM clustering instead of activation functions, while connection weights are formed as the linear functions and their coefficients are estimated by the local least squares estimator (LLSE)-based learning. Because the raw dataset collected from Raman spectroscopy include high-dimensional variables over about three thousands, principal component analysis(PCA) is applied for the dimensional reduction. In addition, artificial bee colony(ABC), which is one of the evolutionary algorithm, is used in order to identify the architecture and parameters of the proposed network. In experiment, the proposed classifier sorts the three kinds of plastics which is the most largely discharged in the real world. The effectiveness of the proposed classifier is proved through a comparison of performance between dataset obtained from chemical analysis and entire dataset extracted directly from Raman spectroscopy.

• 한국정보과학회논문지:소프트웨어및응용
• /
• 제31권5호
• /
• pp.563-569
• /
• 2004

저니키 모멘트(Zernike moment)는 영상의 표현 능력이 뛰어나기 때문에 객체 인식 또는 내용기반 영상 검색 시스템에서 많이 사용되었으나, 정의식이 복잡하기 때문에 많은 연산량을 필요로 하는 단점이 있다. 저니키 모멘트를 빠르게 계산하는 기존의 방법들은 주로 1차원 실수 방사 다항식을 빠르게 계산하는 방법에 중점을 두었다. 본 논문에서는 저니키 복소 기저 함수의 대칭성을 유도하여 저니키 기저함수를 빠르게 계산하고 입력 영상으로부터 저니키 모멘트를 효율적으로 추출하는 방법을 제안한다. 제안하는 방법은 저니키 기저 함수 계산에 필요한 연산량을 기존 방법의 약 20%로 줄이고, 저니키 모멘트 추출에 필요한 곱셈 연산을 25%로 감소시킨다. 또한, 저니키 모멘트를 특징 벡터로 이용하는 시스템 구현 시 필요한 메모리 요구량도 기존 방법의 25%만을 필요로 한다. 제안하는 방법은 회전 모멘트, 의사 저니키 모멘트, ART(Angular Radial Transform) 등의 계산에도 같은 방식으로 적용될 수 있다.

• 전기학회논문지
• /
• 제60권12호
• /
• pp.2352-2360
• /
• 2011

In this paper, we introduce a comprehensive design methodology of Radial Basis Function Neural Networks (RBFNN) that is based on mechanism of clustering and optimization algorithm. We can divide some clusters based on similarity of input dataset by using clustering algorithm. As a result, the number of clusters is equal to the number of nodes in the hidden layer. Moreover, the centers of each cluster are used into the centers of each receptive field in the hidden layer. In this study, we have applied Fuzzy-C Means(FCM) and K-Means(KM) clustering algorithm, respectively and compared between them. The weight connections of model are expanded into the type of polynomial functions such as linear and quadratic. In this reason, the output of model consists of relation between input and output. In order to get the optimal structure and better performance, Particle Swarm Optimization(PSO) is used. We can obtain optimized parameters such as both the number of clusters and the polynomial order of weights connection through structural optimization as well as the widths of receptive fields through parametric optimization. To evaluate the performance of proposed model, NXT equipment offered by National Instrument(NI) is exploited. The situation awareness system-related intelligent model was built up by the experimental dataset of distance information measured between object and diverse sensor such as sound sensor, light sensor, and ultrasonic sensor of NXT equipment.

• 한국인터넷방송통신학회논문지
• /
• 제12권3호
• /
• pp.123-131
• /
• 2012

오래 전부터 연료의 가격은 상승하고 있다. 제조업체는 보증을 통해 실용적인 대안을 찾고자 전기와 강력한 바이오 연료를 이용하여 차량의 성장가능을 연구하고 있다. 이제, 이러한 녹색 환경(emission) 관련된 보증은 보증기간이 확장되며, 이러한 보증을 "수퍼 보증" 이라 불린다. 본 논문의 주요 결과는 라돈 변환의 역행렬을 보증공간의 수치를 줄이기 위해 사용되며, 응용 프로그램 및 RBF 네트워크를 사용하여 대략적인 이변량의 보증 기능에 새로운 방법을 제시한다. 이 방법은 다음과 같은 단계로 구성되어 있다. 첫째, 라돈 변환을 이용하여, 이변량 보증 함수의 1차원 함수를 줄일 수 있다. 둘째, 1 차원 함수의 각 신경 서브 네트워크와 신경 네트워크 기법을 사용하여 근사할 수 있다. 셋째, 이러한 신경 sub-networks 형태로 최종 근사 신경망 함께 결합 된다. 넷째, 라 돈 변환의 역함수 값을 사용 하여 최종 근사 신경 네트워크에 우리가 주어진 함수 근사화를 얻을 수 있다. 또한, 우리는 자동차 회사의 일부 그린 보증 데이터를 가지고 위의 방법을 적용한다.

• 한국지리정보학회지
• /
• 제13권3호
• /
• pp.29-41
• /
• 2010

본 연구는 미측정점의 값을 모델링하기 위해 사용되는 여러 가지 공간보간방법들의 예측오차를 비교하고 정확성을 검증하였다. 동해안 해안 지역의 표고점을 대상으로 역거리가중법, 크리깅, 지역 다항식보간법, 방사기반함수의 공간보간법과 관련된 매개변수들을 동일한 조건하에서 실행하여 평균제곱근을 산출한 결과, 단순 크리깅 방법의 원형 모델이 가장 작은 값으로 나타났다. 래스터의 연산 결과, 방사기반함수의 다중방정식에 의한 예측 지도가 대상 지역의 불규칙삼각망 표현과 일치정도가 높았다. 또한 공간보간 실행시 선택된 조건하에서 제공되는 최적 파워값을 사용하는 것이 양호한 보간 결과를 얻을 수 있다.

• Communications for Statistical Applications and Methods
• /
• 제15권4호
• /
• pp.543-549
• /
• 2008

Inspired by using a robust loss function in the support vector machine regression to control training error and the idea of robust template matching with M-estimator, Chen (2004) applies M-estimator techniques to gaussian radial basis functions and form a new class of robust kernels for the support vector machines. We are specially interested in the shape of the Huber's M-estimator in this context and propose a way to find the shape parameter of the Huber's M-estimating function. For simplicity, only the two-class classification problem is considered.

• 제어로봇시스템학회논문지
• /
• 제20권12호
• /
• pp.1238-1245
• /
• 2014

This paper proposes an RBFN-based adaptive tracking controller for an electrically driven mobile robot with parametric uncertainties and external disturbances. A mobile robot model considered in this paper includes all models of the robot body and actuators with uncertain kinematic and dynamic parameters, and uncertain frictions and external disturbances. The proposed controller consists of an RBFN(Radial Basis Function Network) and a robust adaptive controller. The presented RBFN is used to approximate unknown nonlinear robot dynamic functions. The proposed controller is adjusted by the adaptation laws obtained through the Lyapunov stability analysis. The proposed control scheme does not a priori need the accurate knowledge of all parameters in the robot kinematics, robot dynamics and actuator dynamics. Also, nominal parameter values are not required in the controller. The global stability of the closed-loop robot control system is guaranteed using the Lyapunov stability theory. Simulation results show the validity and robustness of the proposed control scheme.