• 한국항공우주학회지
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• 제38권8호
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• pp.774-780
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• 2010

본 논문은 $t_{go}$-다항식 유도 법칙에서 가속도 제한을 고려한 유도이득(guidance gain)을 선정하는 방법을 다룬다. 다항식 유도 법칙은 유도명령의 형태를 $t_{go}$의 다항식 형태로 가정하여 유도 되며 유도이득으로 임의의 양의 실수 값 (Real value)을 선정할 수 있다는 특징을 가지고 있다. 따라서 유도이득의 결정에 따라 큰 가속도 명령이 산출 될 수 있는 가능성이 있어서, 적절한 유도이득을 결정하는데 모호성이 존재하게 된다. 이러한 어려움을 해결하기 위해 본 논문에서 다항식 유도 법칙의 가속도 명령의 닫힌 해를 유도하고, 이로 부터 최대 가속도와 유도이득 간의 관계식을 구하여 가속도 제한을 넘지 않는 유도이득을 선정하는 방법을 제안한다. 최종적으로 시뮬레이션을 통해 제안한 방법을 검증한다.

• Journal of the Korean Data and Information Science Society
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• 제27권5호
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• pp.1433-1441
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• 2016

Small area model provides reliable and accurate estimations when the sample size is not sufficient. Our dataset has an inherent nonlinear pattern which signicantly affects our inference. In this case, we could consider semiparametric models such as truncated polynomial basis function and radial basis function. In this paper, we study four Bayesian semiparametric models for small areas to handle this point. Four small area models are based on two kinds of basis function and different knots positions. To evaluate the different estimates, four comparison measurements have been employed as criteria. In these comparison measurements, the truncated polynomial basis function with equal quantile knots has shown the best result. In Bayesian calculation, we use Gibbs sampler to solve the numerical problems.

• 한국지능시스템학회논문지
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• 제15권2호
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• pp.236-244
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• 2005

본 연구에서는 퍼지관계 및 진화론적 최적 다층 퍼셉트론에 기초한 퍼지다항식 뉴럴네트워크(FPNN)의 새로운 구조를 소개하고, 포괄적인 설계방법론을 토의하며, 그리고 일련의 수치적인 실험이 수행된다. 진화론적 최적 FPNN(EFPNN)의 구축을 위해 컴퓨터지능(CI)의 기반 기술을 이용한다. EFPNN의 구조는 규칙베이스 퍼지뉴럴네트워크와 다항식 뉴럴네트워크의 결합에 의한 유전자 최적 구동 하이브리드 시스템의 시너지 이용으로 얻어진다. 퍼지뉴럴네트워크는 EFPNN의 전체규칙 구조의 전반부에 기여하고, EFPNN의 후반부는 다항식 뉴럴네트워크를 사용하여 설계된다. EFPNN의 후반부를 위한 유전론적 최적 다항식 뉴럴네트워크의 개발은 두 최적화 기법에 의해 수행된다. 즉 구조적 최적화는 유전자알고리즘에 의해 수행되고, 파라미터 최적화는 최소자승법 기반의 학습을 통해 행하여진다. EFPNN의 성능 평가를 위해, 모델은 몇 가지 수치 예제를 이용한다. 비교에 의한 해석은 제안된 EFPNN이 이전에 제시된 다른 지능형 모델보다 높은 정확도 뿐만 아니라 좀 더 우수한 예측능력을 가지는 모델임을 보여준다.

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

• 전기학회논문지
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• 제61권5호
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• pp.744-752
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• 2012

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as an one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problems. In data preprocessing part, Principal Component Analysis(PCA) which is generally used in face recognition, which is useful to express some classes using reduction, since it is effective to maintain the rate of recognition and to reduce the amount of data at the same time. However, because of there of the whole face image, it can not guarantee the detection rate about the change of viewpoint and whole image. Thus, to compensate for the defects, Linear Discriminant Analysis(LDA) is used to enhance the separation of different classes. In this paper, we combine the PCA&LDA algorithm and design the optimized pRBFNNs for recognition module. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as two kinds of polynomials such as constant, and linear. The coefficients of connection weight identified with back-propagation using gradient descent method. The output of the pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to face image(ex Yale, AT&T) datasets and then demonstrated from the viewpoint of the output performance and recognition rate.

• 전기학회논문지
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• 제64권1호
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• pp.99-106
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• 2015

In this study, we propose Radial basis function Neural Network(RBFNN) using Recursive Weighted Least Square Estimation(RWLSE) to effectively deal with big data class meteorological radar data. In the condition part of the RBFNN, Fuzzy C-Means(FCM) clustering is used to obtain fitness values taking into account characteristics of input data, and connection weights are defined as linear polynomial function in the conclusion part. The coefficients of the polynomial function are estimated by using RWLSE in order to cope with big data. As recursive learning technique, RWLSE which is based on WLSE is carried out to efficiently process big data. This study is experimented with both widely used some Machine Learning (ML) dataset and big data obtained from meteorological radar to evaluate the performance of the proposed classifier. The meteorological radar data as big data consists of precipitation echo and non-precipitation echo, and the proposed classifier is used to efficiently classify these echoes.

• 한국도로학회논문집
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• 제15권6호
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• pp.103-111
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• 2013

PURPOSES : Previous Speed Profile reflects the patterns of speeds in sections of tangents to curves in the roads. However these patterns are uniform of speeds and Acceleration/Deceleration. In oder to supplement these shortcomings. this study made a new profile which can contain factors of Acceleration/Deceleration through theories of Previous Speed Profiles. METHODS : For sakes, this study developed the speed prediction model of Rural Multi-Lane Highways and calculated Acceleration/Deceleration by appling a Polynomial model based on developed speed prediction model. Polynomial model is based on second by second. Acceleration/Deceleration Profile is developed with the various scenarios of road geometric conditions. RESULTS : The longer an ahead tangent length is, The higher an acceleration rate in curve occurs due to wide sight distance. However when there are big speed gaps between two curves, the longer tangent length alleviate acceleration rate. CONCLUSIONS : Acceleration/Deceleration Profile can overview th patterns of speeds and Accelerations/Decelerations in the various road geometric conditions. Also this result will help road designer have a proper guidance to exam a potential geometric conditions where may occur the acceleration/deceleration states.

• 한국항공우주학회지
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• 제45권8호
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• pp.662-670
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• 2017

본 논문에서는 한국형 달 착륙 임무를 위한 아폴로 유도 법칙의 파라미터 선정을 위한 최적화 기반의 절차를 제안하였다. 달 착륙 문제를 연료 소모량을 최소화하기 위한 궤적 최적화 문제로 공식화하였으며 비행 이전 단계에서 본 문제를 풀어 착륙선의 기준 궤적을 획득할 수 있다. 아폴로 유도의 파라미터들은 유도 명령을 정의하기 위해 사용되는 다항식의 계수들이며, 비행 이전 단계에서 구해진 기준 궤적을 기반으로 선정된다. 제안된 절차의 효과를 입증하기 위해, 본 절차를 사용한 한국형 달 착륙 임무의 착륙 유도 사례연구를 수행하였다.

• 한국CDE학회논문집
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• 제5권2호
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• pp.127-135
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• 2000

Emergent computing paradigms such as genetic algorithms have found increased use in problems in engineering design. These computational tools have been shown to be applicable in the solution of generically difficult design optimization problems characterized by nonconvexities in the design space and the presence of discrete and integer design variables. Another aspect of these computational paradigms that have been lumped under the bread subject category of soft computing, is the domain of artificial intelligence, knowledge-based expert system, and machine learning. The paper explores a machine learning paradigm referred to as teaming classifier systems to construct the high-quality global function approximations between the design variables and a response function for subsequent use in design optimization. A classifier system is a machine teaming system which learns syntactically simple string rules, called classifiers for guiding the system's performance in an arbitrary environment. The capability of a learning classifier system facilitates the adaptive selection of the optimal number of training data according to the noise and multimodality in the design space of interest. The present study used the polynomial based response surface as global function approximation tools and showed its effectiveness in the improvement on the approximation performance.

• Asian-Australasian Journal of Animal Sciences
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• 제24권1호
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• pp.17-27
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• 2011

A selection experiment for reduced residual feed intake (RFI) in Yorkshire pigs consisted of a line selected for lower RFI (LRFI) and a random control line (CTRL). Longitudinal measurements of daily feed intake (DFI) and body weight (BW) from generation 5 of this experiment were used. The objectives of this study were to evaluate the use of random regression (RR) and nonlinear mixed models to predict DFI and BW for individual pigs, accounting for the substantial missing information that characterizes these data, and to evaluate the effect of selection for RFI on BW and DFI curves. Forty RR models with different-order polynomials of age as fixed and random effects, and with homogeneous or heterogeneous residual variance by month of age, were fitted for both DFI and BW. Based on predicted residual sum of squares (PRESS) and residual diagnostics, the quadratic polynomial RR model was identified to be best, but with heterogeneous residual variance for DFI and homogeneous residual variance for BW. Compared to the simple quadratic and linear regression models for individual pigs, these RR models decreased PRESS by 1% and 2% for DFI and by 42% and 36% for BW on boars and gilts, respectively. Given the same number of random effects as the polynomial RR models, i.e., two for BW and one for DFI, the non-linear Gompertz model predicted better than the polynomial RR models but not as good as higher order polynomial RR models. After five generations of selection for reduced RFI, the LRFI line had a lower population curve for DFI and BW than the CTRL line, especially towards the end of the growth period.