• Journal of Electrical Engineering and Technology
• /
• 제11권2호
• /
• pp.265-273
• /
• 2016

A water wall system is one of the most important components of a boiler in a thermal power plant, and it is a nonlinear Multi-Input and Multi-Output (MIMO) system, with 6 inputs and 3 outputs. Three models are developed and comp for the controller design, including a linear model, a multilayer feed-forward neural network (MFNN) model and an Echo State Network (ESN) model. First, the linear model is developed by linearizing a given nonlinear model and is analyzed as a function of the operating point. Second, the MFNN and the ESN are developed by using training data from the nonlinear model. The three models are validated using Matlab with nonlinear input-output data that was not used during training.

• 한국환경과학회지
• /
• 제15권2호
• /
• pp.141-155
• /
• 2006

This study is aimed at the development of a runoff forecasting model to solve the uncertainties occurring in the process of rainfall-runoff modeling and improve the modeling accuracy of the stream runoff forecasting, The study area is the downstream of Naeseung-chun. Therefore, time-dependent data was obtained from the Wolpo water level gauging station. 11 and 2 out of total 13 flood events were selected for the training and testing set of model. The model performance was improved as the measuring time interval$(T_m)$ was smaller than the sampling time interval$(T_s)$. The Neuro-Fuzzy(NF) and TANK models can give more accurate runoff forecasts up to 4 hours ahead than the Feed Forward Multilayer Neural Network(FFNN) model in standard above the Determination coefficient$(R^2)$ 0.7.

• Advances in Computational Design
• /
• 제5권3호
• /
• pp.291-304
• /
• 2020

In this paper, we develop models to design the airfoil using Multilayer Feed-forward Artificial Neural Network (MFANN) and Support Vector Regression model (SVR). The aerodynamic coefficients corresponding to series of airfoil are stored in a database along with the airfoil coordinates. A neural network is created with aerodynamic coefficient as input to produce the airfoil coordinates as output. The performance of the models have been evaluated. The results show that the SVR model yields the lowest prediction error.

• 지능정보연구
• /
• 제14권3호
• /
• pp.25-39
• /
• 2008

본 연구는 인공신경망을 이용하여 대대급 방어 작전에서 임의시점에서의 작전지속능력을 예측하는 데 있다. 전투결과에 대한 수학적 모델링은 이를 위한 많은 요인들이 가지는 시?공간적 가변성으로 인해 전투력을 평가하는데 많은 문제점이 있었다. 따라서 이번 연구에서는 대대 전투지휘훈련간 각 부대의 생존률을 전방향 다층 신경망(Feed-Forward Multilayer Perceptrons, MLP)과 일반 회귀신경망(General Regression Neural Network, GRNN)모형에 적용하여 임무달성 여부를 예측하였다. 실험 결과 매개변수들의 비선형적인 관계에도 불구하고 각각 82.62%, 85.48%의 적중률을 보여 일반회귀신경망 모형이 지휘관이 상황을 인식하고 예비대 투입 우선순위 선정 등 실시간 지휘결심을 하는데 도움을 줄 수 있는 방법임을 보여준다.

• Structural Engineering and Mechanics
• /
• 제26권4호
• /
• pp.377-392
• /
• 2007

A discrete sliding mode control (SMC) method based on hybrid model of neural network and nominal model is proposed to reduce the vibration of flexible structures, which is a robust active controller developed by using a sliding manifold approach. Since the thick boundary layer will reduce the virtue of SMC, the multilayer feed-forward neural network is adopted to model the uncertainty part. The neural network is trained by Levenberg-Marquardt backpropagation. The design objective of the sliding mode surface is based on the quadratic optimal cost function. In course of running, the input signal of SMC come from the hybrid model of the nominal model and the neural network. The simulation shows that the proposed control scheme is very effective for large uncertainty systems.

• Structural Engineering and Mechanics
• /
• 제58권3호
• /
• pp.459-473
• /
• 2016

Various vague and unstructured problems encountered the civil engineering/designers that persuaded by their experiences. One of these problems is the structural failure of the reinforced concrete (RC) building determination. Typically, using the traditional Limit state method is time consuming and complex in designing structures that are optimized in terms of one/many parameters. Recent research has revealed the Artificial Neural Networks potentiality in solving various real life problems. Thus, the current work employed the Multilayer Perceptron Feed-Forward Network (MLP-FFN) classifier to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistoried RC building structure in the future. In order to evaluate the proposed method performance, a database of 257 multistoried buildings RC structures has been constructed by professional engineers, from which 150 RC structures were used. From the structural design, fifteen features have been extracted, where nine features of them have been selected to perform the classification process. Various performance measures have been calculated to evaluate the proposed model. The experimental results established satisfactory performance of the proposed model.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2005년도 ICCAS
• /
• pp.281-286
• /
• 2005

The main objective of the research work presented in this article is to present a systematic approach for designing a multilayer feed-forward artificial neural network based self-tuning power system stabilizer (ST-ANNPSS). In order to suggest an approach for selecting the number of neurons in the hidden layer, the dynamic performance of the system with ST-ANNPSS is studied and hence compared with that of conventional PSS. Finally the effect of variation of loading condition and equivalent reactance, Xe is investigated on dynamic performance of the system with ST-ANNPSS. Investigations reveal that ANN with one hidden layer comprising nine neurons is adequate and sufficient for ST-ANNPSS. Studies show that the dynamic performance of STANNPSS is quite superior to that of conventional PSS for the loading condition different from the nominal. Also it is revealed that the performance of ST-ANNPSS is quite robust to a wide variation in loading condition.

• 한국정밀공학회지
• /
• 제16권11호
• /
• pp.158-165
• /
• 1999

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure, a method is presented in this paper which uses hybrid neuro-genetic technique. Feed-forward multilayer neural networks trained by back-propagation are used to learn the input)the location and dept of a crack)-output(the structural eigenfrequencies) relation of the structural system. With this neural network and genetic algorithm, it is possible to formulate the inverse problem. Neural network training algorithm is the back propagation algorithm with the momentum method to attain stable convergence in the training process and with the adaptive learning rate method to speed up convergence. Finally, genetic algorithm is used to fine the minimum square error.

• 전자공학회논문지B
• /
• 제30B권11호
• /
• pp.56-65
• /
• 1993

This paper presents the design consideration of the MFNNs(Multilayer Feed forward Neural Networks) based on the distribution of the given teching patterns. By extracting the feature points from the given teaching patterns, the structure of a network including the netowrk size and interconnection weights of a network is initialized. This network is trained based on the modified version of the EBP(Error Back Propagation) algorithm. As a result, the proposed method has the advantage of learning speed compared to the conventional learning of the MFNNs with randomly chosen initial weights. To show the effectiveness of the suggested approach, the simulation result on the approximation of a two demensional continuous function is shown.

• Smart Structures and Systems
• /
• 제13권1호
• /
• pp.81-98
• /
• 2014

This paper presents the elastic buckling of smart lightweight column structures integrated with a pair of surface piezoelectric layers using artificial intelligence. The finite element modeling of Smart lightweight columns is found using $ANSYS^{(R)}$ software. Then, the first buckling load of the structure is calculated using eigenvalue buckling analysis. To determine the accuracy of the present finite element analysis, a compression study is carried out with literature. Later, parametric studies for length variations, width, and thickness of the elastic core and of the piezoelectric outer layers are performed and the associated buckling load data sets for artificial intelligence are gathered. Finally, the application of soft computing-based methods including artificial neural network (ANN), fuzzy inference system (FIS), and adaptive neuro fuzzy inference system (ANFIS) were carried out. A comparative study is then made between the mentioned soft computing methods and the performance of the models is evaluated using statistic measurements. The comparison of the results reveal that, the ANFIS model with Gaussian membership function provides high accuracy on the prediction of the buckling load in smart lightweight columns, providing better predictions compared to other methods. However, the results obtained from the ANN model using the feed-forward algorithm are also accurate and reliable.