• 산업공학
• /
• 제9권3호
• /
• pp.298-305
• /
• 1996

Cold rolling mill process in steel works uses stands of rolls to flatten a strip to a desired thickness. Most of rolling processes use mathematical models to predict rolling force which is very important to decide the resultant thickness of a coil. In general, these mathematical models are not flexible for variant coil types and cannot handle various elements which is practically important to decide accurate rolling force. A corrective neural network is proposed to improve the accuracy of rolling force prediction. Additional variables-composition of the coil, coiling temperature and working roll parameters-are fed to the network. The model uses an MLP with BP to predict a corrective coefficient. The test results using 1,586 process data collected at POSCO in early 1995 show that the proposed model reduced the prediction error by 30% on average.

• 제어로봇시스템학회논문지
• /
• 제4권2호
• /
• pp.209-214
• /
• 1998

In this paper, we are dealing with the problem of controlling unknown nonlinear dynamical system by using neural networks. A novel error self-recurrent(ESR) neural model is presented to perform black-box identification. Through the various outcome of the experiment, a new neural network is seen to be considerably faster than the BP algorithm and has advantages of being less affected by poor initial weights and learning rate. These characteristics make it flexible to design the controller in real-time based on neural networks model. In addition, we design an adaptive PID controller that Keyser suggested by using ESR neural networks, and present a method on the implementation of adaptive controller based on neural network for practical applications. We obtained good results in the case of robot manipulator experiment.

• 한국통신학회논문지
• /
• 제31권10B호
• /
• pp.900-908
• /
• 2006

본 논문에서는 이동성을 보장하면서 다양한 멀티미디어 서비스 및 컨텐츠를 지원하는 서비스들이 거의 동시에 상용화되는 상황에서 향후 이들 서비스의 시장경쟁구도가 어떻게 형성될 것인지에 대해 예측해 보고자 한다. 미래 시장경쟁구도 예측을 위해서 과거 정보통신사업에서 시장자료에 근거하여 판단하는 방법을 지양하고, 소비자 선호에 근거하는 선택확률모형을 적용하는 접근법을 시도함으로써 유사 서비스들의 경쟁양상을 보다 정확하게 분석할 수 있는 계기를 마련하였다. 분석결과, 서비스들간의 대체효과, 유인효과, 외부영향 요인에 따른 변화 양상 등을 예상할 수 있었고, 이들을 근거로 하여 기업전략 방향이나 정부정책 수립에 대해 전반적으로 나아갈 방향을 제시하였다.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2002년도 하계학술대회 논문집 D
• /
• pp.2821-2823
• /
• 2002

In this paper we propose the Multi-FNN (Fuzzy-Neural Networks) for optimal identification modeling of complex system. The proposed Multi-FNNs is based on a concept of FNNs and exploit linear inference being treated as generic inference mechanisms. In the networks learning, backpropagation(BP) algorithm of neural networks is used to updata the parameters of the network in order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM(Hard C-Means)clustering algorithm which carry out the input-output dat a preprocessing function and Genetic Algorithm which carry out optimization of model The HCM clustering method is utilized to determine the structure of Multi-FNNs. The parameters of Multi-FNN model such as apexes of membership function, learning rates, and momentum coefficients are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization abilities of the model. NOx emission process data of gas turbine power plant is simulated in order to confirm the efficiency and feasibility of the proposed approach in this paper.

• 전기학회논문지
• /
• 제64권10호
• /
• pp.1469-1478
• /
• 2015

In this paper, The methodology of Type-2 fuzzy set-based Radial Basis Function Neural Network(T2RBFNN) is proposed for Sewage Treatment Process and the simulator is developed for application to the real-world sewage treatment plant by using the proposed model. The proposed model has robust characteristic than conventional RBFNN. architecture of network consist of three layers such as input layer, hidden layer and output layer of RBFNN, and Type-2 fuzzy set is applied to receptive field in contrast with conventional radial basis function. In addition, the connection weights of the proposed model are defined as linear polynomial function, and then are learned through Back-Propagation(BP). Type reduction is carried out by using Karnik and Mendel(KM) algorithm between hidden layer and output layer. Sewage treatment data obtained from real-world sewage treatment plant is employed to evaluate performance of the proposed model, and their results are analyzed as well as compared with those of conventional RBFNN.

• International Journal of Naval Architecture and Ocean Engineering
• /
• 제12권1호
• /
• pp.314-324
• /
• 2020

Due to the nonlinearity and environmental uncertainties, the design of the ship's steering controller is a long-term challenge. The purpose of this study is to design an intelligent autopilot based on Extended Kalman Filter (EKF) trained Radial Basis Function Neural Network (RBFNN) control algorithm. The newly developed free running model scaled surface vessel was employed to execute the motion control experiments. After describing the design of the EKF trained RBFNN autopilot, the performances of the proposed control system were investigated by conducting experiments using the physical model on lake and simulations using the corresponding mathematical model. The results demonstrate that the developed control system is feasible to be used for the ship's motion control in the presences of environmental disturbances. Moreover, in comparison with the Back-Propagation (BP) neural networks and Proportional-Derivative (PD) based control methods, the EKF RBFNN based control method shows better performance regarding course keeping and trajectory tracking.

• 한국농림기상학회지
• /
• 제12권2호
• /
• pp.107-121
• /
• 2010

본 연구에서는 한반도의 주요 생태계인 활엽수림과 농경지에서 지면 모형 JULES(Joint UK Land Environment Simulator)으로 모의한 총일차생산량 (Gross Primary Productivity: GPP)과 생태계 호흡량 (ecosystem respiration: RE)의 수치 모사 결과에 영향을 미치는 주요 모수를 파악하였으며, 민감한 모수에 대해 실측자료를 사용함에 따른 모형 예측력의 개선 정도를 평가하였다. 민감도 실험의 결과, 활엽수림과 농경지에서 모두 JULES로 모의한 GPP는 잎 내부의 질소농도와 리불로오스이인산(RuBP) 카르복실화의 최대 속도에 가장 민감하였다. RE는 활엽수림에서는 목질부 탄소량과 엽면적지수를 연결시켜주는 상수에 가장 민감하였다. 반면에 농경지에서 수치모사된 RE는 GPP와 같이 각각 잎 내부의 질소 농도와 RuBP 카르복실화의 최대 속도에 가장 민감하였다. JULES로부터 제공된 모수의 값으로 모의된 두 지역의 GPP와 RE는 모두 관측값에 비해 과대평가되었다. 특히 활엽수림에서 GPP가 가장 민감하게 반응했던 잎 질소 농도의 실제 관측값이 모형에서 사용하는 기존 설정값의 50% 이하임을 고려할 때 모형에서 설정된 모수의 값으로 탄소 순환을 수치 모사할 경우에 모수의 기존 설정값과 실제값의 차이가 모형의 과다모의에 상당한 영향을 미침을 확인할 수 있었다. 따라서 한반도 탄소순환의 현실적인 모의를 위해서는 모형에서 요구되는 생물리학적 정보가 한반도 다양한 식생 기능 형태를 현실적으로 잘 반영하는지를 확인해야 할 뿐 아니라 지속적인 현장 관측을 통해서 생물리학적 정보와 관련된 자료기반을 마련하는 것이 중요하다.

• 산업과 과학
• /
• 제3권2호
• /
• pp.8-16
• /
• 2024

본 논문에서는 인구통계학적, 생리학적 특성을 기반으로 환자에게 가장 적합한 약물을 예측하는 것을 목표로 하는 약물 분류 시스템을 제시한다. 데이터 세트에는 적절한 약물을 결정하기 위한 목적으로 연령, 성별, 혈압(BP), 콜레스테롤 수치, 나트륨 대 칼륨 비율(Na_to_K)과 같은 속성들이 포함된다. 본 연구에 사용된 모델은 KNN(K-Nearest Neighbors), 로지스틱 회귀 분석 및 Random Forest이다. 하이퍼파라미터를 최적화하기 위해 5겹 교차 검증을 갖춘 GridSearchCV를 활용하였으며, 각 모델은 데이터 세트에서 훈련 및 테스트 되었다. 초매개변수 조정 유무에 관계없이 각 모델의 성능은 정확도, 혼동 행렬, 분류 보고서와 같은 지표를 사용하여 평가되었다. GridSearchCV를 적용하지 않은 모델의 정확도는 0.7, 0.875, 0.975인 반면, GridSearchCV를 적용한 모델의 정확도는 0.75, 1.0, 0.975로 나타났다. GridSearchCV는 로지스틱 회귀 분석을 세 가지 모델 중 약물 분류에 가장 효과적인 모델로 식별했으며, K-Nearest Neighbors가 그 뒤를 이었고 Na_to_K 비율은 결과를 예측하는 데 중요한 특징인 것으로 밝혀졌다.

• 한국방송∙미디어공학회:학술대회논문집
• /
• 한국방송공학회 2009년도 IWAIT
• /
• pp.547-550
• /
• 2009

The object recognition mechanism of human being is not well understood yet. On research of animal experiment using an ape, however, neurons that respond to simple shape (e.g. circle, triangle, square and so on) were found. And Hypothesis has been set up as human being may recognize object as combination of such simple shapes. That mechanism is called Figure Alphabet Hypothesis, and those simple shapes are called Figure Alphabet. As one way to research object recognition algorithm, we focused attention to this Figure Alphabet Hypothesis. Getting idea from it, we proposed the feature extraction algorithm for object recognition. In this paper, we described recognition of binarized images of multifont alphabet characters by the recognition model which combined three-layered neural network in the feature extraction algorithm. First of all, we calculated the difference between the learning image data set and the template by the feature extraction algorithm. The computed finite difference is a feature quantity of the feature extraction algorithm. We had it input the feature quantity to the neural network model and learn by backpropagation (BP method). We had the recognition model recognize the unknown image data set and found the correct answer rate. To estimate the performance of the contriving recognition model, we had the unknown image data set recognized by a conventional neural network. As a result, the contriving recognition model showed a higher correct answer rate than a conventional neural network model. Therefore the validity of the contriving recognition model could be proved. We'll plan the research a recognition of natural image by the contriving recognition model in the future.

• Journal of Advanced Marine Engineering and Technology
• /
• 제27권1호
• /
• pp.24-32
• /
• 2003

The PID controller has been widely used in industrial applications due to its simple structure and robustness. Even if it is initially well tuned, the PID controller must be retuned to maintain acceptable performance when there are system parameter changes due to the change of operation conditions. In this paper, a self-tuning control scheme which comprises a parameter estimator, a NN-based rule emulator and a PID controller is proposed, which can cope with changing environments. This method involves combining neural networks and real-coded genetic algorithms(RCGAs) with conventional approaches to provide a stable and satisfactory response. A RCGA-based parameter estimation method is first described to obtain the first-order with time delay model from over-damped high-order systems. Then, a set of optimum PID parameters are calculated based on the estimated model such that they cover the entire spectrum of system operations and an optimum tuning rule is trained with a BP-based neural network. A set of simulation works on systems with time delay are carried out to demonstrate the effectiveness of the proposed method.