• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.10A
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• pp.1639-1644
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• 2001

Two decision feedback equalizer structures employing recurrent neural network (RNN) used for non-linear channels with severe intersymbol interference (ISI) and non-linear distortion are proposed in this paper, which skillfully put the traditional decision feedback structure for linear channels equalization into RNN, replace decision feedback signal with training signal in the learning process and adaptively adjust the learning step. Simulative results of the first type of two new equalizer structures have shown that it has better equalization performances than traditional recurrent neural network equalizer (RNNE) under the same condition.

• Structural Engineering and Mechanics
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• v.23 no.2
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• pp.177-193
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• 2006

Determining the hysteretic energy demand and dissipation capacity and level of damage of the structure to a predefined earthquake ground motion is a highly non-linear problem and is one of the questions involved in predicting the structure's response for low-performance levels (life safe, near collapse, collapse) in performance-based earthquake resistant design. Neural Network (NN) analysis offers an alternative approach for investigation of non-linear relationships in engineering problems. The results of NN yield a more realistic and accurate prediction. A NN model can help the engineer to predict the seismic performance of the structure and to design the structural elements, even when there is not adequate information at the early stages of the design process. The principal aim of this study is to develop and test multi-layered feedforward NNs trained with the back-propagation algorithm to model the non-linear relationship between the structural and ground motion parameters and the hysteretic energy demand in steel moment resisting frames. The approach adapted in this study was shown to be capable of providing accurate estimates of hysteretic energy demand by using the six design parameters.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.2
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• pp.63-68
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• 2004

This paper presents an approach to optimum design of Moving Magnet Type Linear Oscillatory Actuator(MM-LOA). The Finite Element Method is applied to characteristic parameters for characteristic analysis and in order to reduce modeling time and efforts, the moving model node technique is used. In addition the neural network is used to reduce computational time of analysis according to changing design variable. To confirm the validity of this study, optimum design results are compared with results of analysis procedure that is verified by experiment.

• Current Optics and Photonics
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• v.4 no.4
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• pp.310-316
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• 2020

We propose and experimentally demonstrate an all-optical radio frequency (RF) spectrum broadening system based on time compression. By utilizing the procedure of dispersion compensation values, the frequency domain is broadened by compressing the linear chirp optical pulse which has been multiplexed by the radio frequency. A detailed mathematical description elucidates that the time compression is a very preferred scheme for spectrum broadening. We also report experimental results to prove this method, magnification factor at 2.7, 8 and 11 have been tested with different dispersion values of fiber, the experimental results agree well with the theoretical results. The proposed system is flexible and the magnification factor is determined by the dispersion values, the proposed scheme is a linear system. In addition, the influence of key parameters, for instance optical bandwidth and the sideband suppression ratio (SSR), are discussed. Magnification factor 11 of the proposed system is demonstrated.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.5
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• pp.526-531
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• 1997

To control nonlinear chemical processes, a new neural linearizing control scheme is proposed. This is a hybrid of a radial basis function(RBF) network and a linear controller, thus the control action applied to the process is the sum of both control actions. Firstly, to train the RBF newtork a linear reference model is determined by analyzing the past operating data of the process. Then, the training of the RBF newtork is iteratively performed to minimize the difference between outputs of the process and the linear reference model. As a result, the apparent dynamics of the process added by the RBF newtork becomes similar to that of the linear reference model. After training, the original nonlinear control problem changes to a linear one, and the closed-loop control performance is improved by using the optimum tuning parameters of the linear controller for the linear dynamics. The proposed control scheme performs control and training simultaneously, and shows a good control performance for nonlinear chemical processes.

• Journal of Digital Convergence
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• v.10 no.9
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• pp.357-362
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• 2012

We have compared and predicted for non-linear time series data which are real data having different variences using GRCA(1) model and neural network method. In particular, using Korea Composite Stock Price Index rate, mean square errors of prediction are obtained in genaralized random coefficient autoregressive model and neural network method. Neural network method prove to be better in short-term forecasting, however GRCA(1) model perform well in long-term forecasting.

• Journal of the Korea Institute of Building Construction
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• v.11 no.1
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• pp.1-8
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• 2011

Linear scheduling method limits to present works of work breakdown structure as a form of lines and was often developed manually. In other words, linear schedule could not utilize activity, work breakdown structure, and etc. information of network schedule such as CPM(Critical Path Method) and has been used only for reporting or confirming construction master plan. Therefore, it is necessary to develop system which can automatically generating the linear schedule based on the network schedule having many accumulated and useful construction schedule information. Thus, this research has an effort to establish data process model, data flow diagram, and data model in order to make linear schedule. In addition, this research addresses the system solution structure, user interface class diagram and logic diagram, and data type schema. The results of this paper can be used as a preliminary research for developing linear schedule generating system prototype by utilizing the network schedule information.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.635-639
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• 2004

In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the modeling of chaotic nonlinear systems. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting the fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet transform. The basic idea of our wavelet based FNN is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. And our network can automatically identify the fuzzy rules by modifying the connection weights of the networks via the gradient descent scheme. To verify the efficiency of our network structure, we evaluate the modeling performance for chaotic nonlinear systems and compare it with those of the FNN and the WFM.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.1
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• pp.111-118
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• 2004

In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the solution of the tracking problem for mobile robots. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting the fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet transform. The basic idea of our wavelet based FNN is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. And our network can automatically identify the fuzzy rules by modifying the connection weights of the networks via the gradient descent scheme. To verify the efficiency of our network structure, we evaluate the tracking performance for mobile robot and compare it with those of the FNN and the WFM.

• Journal of Korea Planning Association
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• v.54 no.3
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• pp.15-26
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• 2019

The development of science and transportation technology leads to the increase of inter - city networks that play an important role in urban growth. Overall, numerous studies based on network theory pay attention to positive effects of urban network on urban growth. However, some studies have pointed out the negative effects of inter-city interactions such as straw effects. This implies that the network between cities may not be positively correlated with urban growth, and that the direction of the influence may vary from a certain threshold, such as the marginal utility curve. In this context, the purpose of this study is to measure the impacts of network with central city on urban growth in the capital region and examine the relationship between urban network and growth. Two multiple regression models are employed with changes in population and employment as dependent variables. The urban network index and other control variables are used as independent variables. Especially, the urban network indexes are used in quadratic forms to examine non linear relations with urban growth such U-shape or an inverted U-shape. The results show that the relationships between networks with the central city and urban growth are not a simple linear, and the influence can be changed from the critical point.