• Journal of Advanced Marine Engineering and Technology
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• 제34권7호
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• pp.971-980
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• 2010

In this paper, an artificial neural network (ANN) was employed to build a predicting model for refrigerator structure. The predicting model includes three input variables of the plaque depth (D), width (W) and interval distance(S) on the inner wall. Finite element method was utilized to obtain the data, which would be necessary for the ANN training process. Finally, a genetic algorithm (GA) was applied to find the optimal parameters that leaded to the minimum inner case deformation under operating condition. The optimal combination found is the depth(D) of 2.63mm, the width(W) of 19.24mm and the interval distance(S) of 49.38mm which leaded to the smallest deformation of 1.88mm for the given refrigerator model.

• 대한토목학회논문집
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• 제30권4D호
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• pp.351-360
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• 2010

2010년 말 현재 우리나라의 자동차등록대수는 1,748만 대에 육박할 정도로 비약적인 증가를 보이고 있다. 자동차의 급격한 증가는 오늘날 우리가 직면한 심각한 사회문제 중 하나인 교통사고를 증가시키고, 이로 인해 인명피해 및 경제적 손실을 초래하고 있다. 이에 본 연구는 유전자 알고리즘과 신경망 이론의 결합에 의한, 향상된 신호교차로 위험도를 예측하는 모형을 개발하여, 장래 교통사고 안전대책 수립시 근간이 되는 기초자료를 제공함으로써, 교통사고를 줄이는데 도움이 되고자 한다. 본 연구에서는, 첫 번째로 교통사고와 교통혼잡이 빈번하게 발생하는 신호교차로를 대상으로 접근로별 교통량과 도로 기하구조 요소를 파악하였고, 교통사고와 교통상충간의 순위상관관계분석을 실시하여 통계적 유의성을 파악하였으며, 교통사고와 교통상충을 적용한 선형회귀모형을 구축하였다. 두 번째로, 유전자 알고리즘과 신경망 이론의 결합에 의한 신호교차로 위험도 예측모형은 신호교차로 교통량 및 도로 기하구조 요소, 교통상충의 특성변수를 적용하여 개발하였다. 마지막으로, 신호교차로 교통사고건수 실측값과 개발모형의 예측값에 대한 적합도 분석을 통해 신뢰수준을 검증한 결과, 개발모형의 신뢰도와 정확도가 기존의 모형에 비해 우수한 것으로 나타났다. 결론적으로, 향후 본 연구를 통해 개발된 교통사고위험도 예측모형을 신호교차로 교통안전정책 수립과 교통안전개선사업에 사용할 경우, 전반적으로 교통안전관련사업의 비용/효율성을 극대화할 수 있을 것으로 기대된다.

• 한국방재학회 논문집
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• 제10권6호
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• pp.81-87
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• 2010

유역에서의 정확한 유출을 계산하기 위해서는 강우-유출현상의 비선형성을 고려해야한다. 그러나 대부분의 강우-유출모형이 선형성을 가정하고 있거나 해석하기가 복잡하여, 비선형성을 고려하면서도 비교적 간단히 계산이 가능한 저류함수법을 유출계산에 이용해오고 있다. 저류함수법은 강수특성과 유역특성에 따라 달라지는 5개의 매개변수를 포함하고 있으며, 주로 유역별로 개발된 회귀식이나 유전자 알고리즘 등 최적화 기법을 이용하여 추정하나, 그 정확한 산정이 어렵다. 그러므로 본 연구에서는 인공 신경망과 유전자 알고리즘을 이용한 Metamodel을 이용하여 매개변수 최적화를 시도하였다. 제안된 기법은 두 단계로 나누어지는데, 첫 번째 단계에서는 기존의 강우-유출관계를 인공신경망에 입력하여 일대일대응 관계를 규명한 후, 두 번째 단계에서는 훈련된 인공신경망과 유전자 알고리즘을 이용하여 강우사상에 대한 저류함수법의 매개변수를 최적화한다. 제안된 모형은 평창강 유역 21개 강우사상에 적용되어 그 적용성을 입증하였다.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 1999년도 추계학술대회-지능형 정보기술과 미래조직 Information Technology and Future Organization
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• pp.323-335
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• 1999

In this paper, we propose a new hybrid model of artificial neural networks(ANNs) and genetic algorithm (GA) to optimal feature transformation and feature weighting. Previous research proposed several variants of hybrid ANNs and GA models including feature weighting, feature subset selection and network structure optimization. Among the vast majority of these studies, however, ANNs did not learn the patterns of data well, because they employed GA for simple use. In this study, we incorporate GA in a simultaneous manner to improve the learning and generalization ability of ANNs. In this study, GA plays role to optimize feature weighting and feature transformation simultaneously. Globally optimized feature weighting overcome the well-known limitations of gradient descent algorithm and globally optimized feature transformation also reduce the dimensionality of the feature space and eliminate irrelevant factors in modeling ANNs. By this procedure, we can improve the performance and enhance the generalisability of ANNs.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권4호
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• pp.302-309
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• 2005

We have studied a model and application that evolves the behavioral knowledge of a virtual robot. The knowledge is represented in classification rules and a neural network, and is learned by a genetic algorithm. The model consists of a virtual robot with behavior knowledge, an environment that it moves in, and an evolution performer that includes a genetic algorithm. We have also applied our model to an environment where the robots gather food into a nest. When comparing our model with the conventional method on various test cases, our model showed superior overall learning.

• 제어로봇시스템학회논문지
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• 제5권3호
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• pp.281-287
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• 1999

In this paper, a design method based on evolutionary programming for feedforward neural networks which have a single hidden layer is presented. By using an evolutionary programming, the network parameters such as the network structure, weight, slope of sigmoid functions and bias of nodes can be acquired simultaneously. To check the effectiveness of the suggested method, two numerical examples are examined. The performance of the identified network is demonstrated.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1998년도 봄 학술발표회 논문집
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• pp.216-225
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• 1998

Although artificial neural network based on backpropagation algorithm is an excellent system simulator, it has still unsolved problems of its structure-decision and learning method. That is, we cannot find a general approach to decide the structure of the neural network and cannot train it satisfactorily because of the local optimum point which it frequently falls into. In addition, although there are many successful applications using backpropagation learning algorithm, there are few efforts to improve the learning algorithm itself. In this study, we suggest a general way to construct the hidden layer of the neural network using binary genetic algorithm and also propose the various learning methods by which the global minimum value of the teaming error can be obtained. A XOR problem and line heating problems are investigated as examples.

• Smart Structures and Systems
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• 제25권2호
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• pp.183-195
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• 2020

Mineral admixtures have been widely used to produce concrete. Pozzolans have been utilized as partially replacement for Portland cement or blended cement in concrete based on the materials' properties and the concrete's desired effects. Several environmental problems associated with producing cement have led to partial replacement of cement with other pozzolans. Furnace slag and fly ash are two of the pozzolans which can be appropriately used as partial replacements for cement in concrete. However, replacing cement with these materials results in significant changes in the mechanical properties of concrete, more specifically, compressive strength. This paper aims to intelligently predict the compressive strength of concretes incorporating furnace slag and fly ash as partial replacements for cement. For this purpose, a database containing 1030 data sets with nine inputs (concrete mix design and age of concrete) and one output (the compressive strength) was collected. Instead of absolute values of inputs, their proportions were used. A hybrid artificial neural network-genetic algorithm (ANN-GA) was employed as a novel approach to conducting the study. The performance of the ANN-GA model is evaluated by another artificial neural network (ANN), which was developed and tuned via a conventional backpropagation (BP) algorithm. Results showed that not only an ANN-GA model can be developed and appropriately used for the compressive strength prediction of concrete but also it can lead to superior results in comparison with an ANN-BP model.

• 산업공학
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• 제25권3호
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• pp.319-326
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• 2012

To effectively predict financial crisis, this paper presents an early warning system based on artificial intelligence technologies. Both Genetic Algorithms and Neural Networks are utilized for the proposed system. First, a genetic algorithm has been developed for the effective selection of economic indices, which are used for monitoring financial crisis. Then, an optimum weight of the selected indices has been determined by a neural network method. To validate the effectiveness of the proposed system, a series of experiments has been conducted by using the Korean economic indices from 2005 to 2008.

• Journal of Electrical Engineering and Technology
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• 제10권1호
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• pp.41-46
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• 2015

It is very important to make accurate forecast of wind power because of its indispensable requirement for power system stable operation. The research is to predict wind power by chaos and BP artificial neural networks (CBPANNs) method based on genetic algorithm, and to evaluate feasibility of the method of predicting wind power. A description of the method is performed. Firstly, a calculation of the largest Lyapunov exponent of the time series of wind power and a judgment of whether wind power has chaotic behavior are made. Secondly, phase space of the time series is reconstructed. Finally, the prediction model is constructed based on the best embedding dimension and best delay time to approximate the uncertain function by which the wind power is forecasted. And then an optimization of the weights and thresholds of the model is conducted by genetic algorithm (GA). And a simulation of the method and an evaluation of its effectiveness are performed. The results show that the proposed method has more accuracy than that of BP artificial neural networks (BP-ANNs).