• 한국컴퓨터산업학회논문지
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• 제3권9호
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• pp.1199-1206
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• 2002

본 연구에서는 일반적인 건물의 공조시스템의 제어에 이용되고 있는 비례-적분(PI)제어의 적용특성을 알아보고 새로운 지능형 제어방식중의 하나인 신경망(neural networks) 제어의 적용가능성을 검토하여 보았다. PI제어에 의한 건물공조와 신경망 제어에 의한 건물공조에 대한 성능을 비교한다. 기존의 PI제어에 의하여 운영되던 건물을 신경망 제어로서 운용하는 경우 기후적, 시스템적 변화에 자체적 대응이 가능한 제어로 적용 가능하다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.941-946
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• 2005

In this paper an active random noise control using adaptive learning rate neural networks is presented. The adaptive learning rate strategy increases the learning rate by a small constant if the current partial derivative of the objective function with respect to the weight and the exponential average of the previous derivatives have the same sign, otherwise the learning rate is decreased by a proportion of its value. The use of an adaptive learning rate attempts to keep the learning step size as large as possible without leading to oscillation. It is expected that a cost function minimize rapidly and training time is decreased. Numerical simulations and experiments of active random noise control with the transfer function of the error path will be performed, to validate the convergence properties of the adaptive learning rate Neural Networks. Control results show that adaptive learning rate Neural Networks control structure can outperform linear controllers and conventional neural network controller for the active random noise control.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.213-217
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• 1990

Neural networks are a class of systems that have many simple processors (neurons) which are highly interconnected. The function of each neuron is simple, and the behavior is determined predominately by the set of interconnections. Thus, a neural network is a special form of parallel computer. Although a major impetus for using neural networks is that they may be able to "learn" the solution to the problem that they are to solve, we argue that another, perhaps even stronger, impetus is that they provide a framework for designing massively parallel machines. The highly interconnected architecture of switching networks suggests similarities to neural networks. Here, we present two switching applications in which neural networks can solve the problems efficiently. We also show that a computational advantage can be gained by using nonuniform time delays in the network.e network.

• 한국해양공학회지
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• 제29권2호
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• pp.111-119
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• 2015

In the initial ship design stage, performance predictions are generally carried out before and after the hull form design. The former is based on the main dimensions and power information, and the latter is based on the geometry of the hull form and propeller. This paper deals with the practical application of neural networks for the prediction of a ship's performance factors before and after the hull form design. For this, the hull form parameters that affect the performance are studied, and an optimal neural network structure based on the SSMB database is constructed. By comparing the results predicted by neural networks and the model test results, we confirmed that neural networks can be applied to practically evaluate the performance in the initial ship design stage.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2008년도 학술발표회 논문집
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• pp.1147-1150
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• 2008

The goal of this research is to apply the neural networks models for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks models consist of the support vector machines neural networks model (SVM-NNM) and multilayer perceptron neural networks model (MLP-NNM), respectively. The SVM-NNM in time series modeling is relatively new and it is more problematic in comparison with classifications. In this study, The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks models, they are composed of training, cross validation, and testing data, respectively. From this research, we evaluate the impact of the SVM-NNM and the MLP-NNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE data from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2002년도 가을 학술발표회 논문집
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• pp.647-652
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• 2002

In the previous study, authors presented the I-ProConS (Intelligent PREdiction system of CONcrete Strength) using artificial neural networks (ANN) that provides in-place strength information of the concrete to facilitate concrete form removal and scheduling for construction. The serious problem of the system has occured, which it cannot appropriately predict the concrete strength when the curing temperature of a curing day is changed. This is because it uses the single neural networks, which all nodes are fully connected, and thus it cannot smoothly respond for external impact. However this paper presents that the problem can be solved by multiple neural networks, which is composed of five ANNs.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(4)
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• pp.242-245
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• 2000

In this paper, we present a color interpolation technique based on artificial neural networks for a single-chip CCD (charge-coupled device) camera with a Bayer color filter array (CFA). Single-chip digital cameras use a color filter array and an interpolation method in order to regenerate high quality color images from sparsely sampled images. We applied 3-layer feedforward neural networks in order to interpolate missing pixel from surrounding pixels. And we compared the proposed method with conventional interpolation methods such as the proposed interpolation algorithm based on neural networks provides a better performance than the conventional interpolation algorithms.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2002년도 11월 학술대회지
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• pp.115-118
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• 2002

Neural networks have been known to have great discriminative power in pattern classification problems. In this paper, the multilayer perceptron neural networks are employed to automatically detect laryngeal pathology in speech. Also new feature parameters are introduced which can reflect the periodicity of speech and its perturbation. These parameters and cepstral coefficients are used as input of the multilayer perceptron neural networks. According to the experiment using Korean disordered speech database, incorporation of new parameters with cepstral coefficients outperforms the case with only cepstral coefficients.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
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• pp.411-415
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• 1994

An adaptive learning control scheme by use of multilayer neural networks for compensating for uncertainties in nonlinear dynamic system is examined. Multilayer neural networks are introduced to map the uncertainties in nonlinear dynamics and perform nonlinear state feedback. Parameters of neural networks are adjusted by conventional back-propagation algorithms modified with the projection operation. Effectiveness of the proposed scheme for tracking control are demonstrated through computer simulations.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
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• pp.134-139
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• 1994

In this paper the comparison between the neural networks and traditional approaches as system identification method are considered. Two model structures of neural networks are the state space model and the input output model neural networks. The traditional methods are the AutoRegressive eXogeneous Input model and the Nonlinear AutoRegressive eXogeneous Input model. The examples considered do not represent any physical system, no a priori knowledge concerning their structure has been used in the identification process. Testing inputs for comparison are the sinusoidal, ramp and the noise ramp.