• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.302-305
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• 1996

In this paper, an optimal identification method using fuzzy-neural networks is proposed for modeling of nonlinear complex systems. The proposed fuzzy-neural modeling implements system structure and parameter identification using the intelligent schemes together with optimization theory, linguistic fuzzy implication rules, and neural networks(NNs) from input and output data of processes. Inference type for this fuzzy-neural modeling is presented as simplified inference. To obtain optimal model, the learning rates and momentum coefficients of fuzz-neural networks(FNNs) and parameters of membership function are tuned using genetic algorithm(GAs). For the purpose of its application to nonlinear processes, data for route choice of traffic problems and those for activated sludge process of sewage treatment system are used for the purpose of evaluating the performance of the proposed fuzzy-neural network modeling. The show that the proposed method can produce the intelligence model w th higher accuracy than other works achieved previously.

• The Transactions of the Korea Information Processing Society
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• v.4 no.8
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• pp.2070-2079
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• 1997

In this paper, we proposed the FNCAC (fuzzy neural call admission control) scheme of the ATM networks which used the benefits of fuzzy logic controller and the learning abilities of the neural network to solve the call admission control problems. The new call in ATM networks is connected if QoS(quality of service) of the current calls is not affected due to the connection of a new call. The neural network CAC(call admission control) system is predictable system because the neural network is able to learn by the input/output pattern. We applied the fuzzy inference on the learning rate and momentum constant for improving the learning speed of the fuzzy neural network. The excellence of the proposed algorithm was verified using measurement of learning numbers in the traditional neural network method and fuzzy neural network method by simulation. We found that the learning speed of the FNCAC based on the fuzzy learning rules is 5 times faster than that of the CAC method based on the traditional neural network theory.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.1
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• pp.45-53
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• 1997

This paper presents hybrid expert system for diagnosis of electric power transformer faults. The expert system diagnose and detect faults in oil-filled power transformers based on dissolved gas analysis. As the preprocessing stage, fuzzy information theory is used to manage the uncertainty in transformer fault diagnosis using dissolved gas analysis. The Kohonen neural network takes the interim results by applying fuzzy informations theory as inputs, and performs the transformer fault diagnosis. The Proposed system tested gas records of power transformers from Korea Electric Power Corporation to verify the diagnosis performance of transformer faults.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.6
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• pp.18-25
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• 2001

KBANN (knowledge-based artificial neural network) combining the analytical learning and the inductive learning has been shown to be more effective than other machine learning models. However KBANN doesn't have the theory refinement ability because the topology of network can't be altered dynamically. Although TopGen was proposed to extend the ability of KABNN in this respect, it also had some defects. The algorithms which could solve this TopGen's defects, enabling the refinement of theory, by extending KBANN, are designed.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.8
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• pp.1939-1951
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• 1994

This study introduces a new tool breakage detecting technology comprised of an unsupervised neural network combined with adaptive time series autoregressive(AR) model where parameters are estimated recursively at each sampling instant using a parameter adaptation algorithm based on an RLS(Recursive Least Square). Experiment indicates that AR parameters are good features for tool breakage, therefore it can be detected by tracking the evolution of the AR parameters during milling process. an ART 2(Adaptive Resonance Theory 2) neural network is used for clustering of tool states using these parameters and the network is capable of self organizing without supervised learning. This system operates successfully under the wide range of cutting conditions without a priori knowledge of the process, with fast monitoring time.

• Journal of the Korea Society of Computer and Information
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• v.10 no.4 s.36
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• pp.17-27
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• 2005

In this study, it proposes the neural network system for character recognition and restoration. Proposes system composed by recognition part and restoration part. In the recognition part. it proposes model of effective pattern recognition to improve ART Neural Network's performance by restricting the unnecessary top-down frame generation and transition. Also the location feature extraction algorithm which applies with Hangeul's structural feature can apply the recognition. In the restoration part, it composes model of inputted image's restoration by Hopfield neural network. We make part experiments to check system's performance, respectively. As a result of experiment, we see improve of recognition rate and possibility of restoration.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.12
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• pp.1249-1255
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• 2004

A new parameter adaptation scheme for RBF Neural Network (NN) has been developed in this paper. Even though the RBF Neural Network (NN) based controllers are robust against both un-modeled dynamics and external disturbances, the performance is not satisfactory for a fast and precise mobile robot. To improve the tracking performance as well as robustness, all the parameters of RBF NN are updated in real time. The stability of this control law is rigorously proved by following the Lyapunov stability theory and shown by the experimental simulations. The fact that all of the weighting factors, width and center of RBF NN have been updated implies that this scheme utilizes all the possibilities in RBF NN to make the controller robust and precise while the mobile robot is following un-known trajectories. The performance of this new algorithm has been compared to the conventional RBF NN controller where some of the parameters are adjusted for robustness.

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2775-2778
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• 2003

Neural network-based fault diagnosis algorithm to detect and isolate faults in the nonlinear systems is proposed. In the proposed method, the fault is detected when the errors between the system output and the neural network nominal system output cross a predetermined threshold. Once a fault in the system is detected, the system outputs are transferred to the fault classifier by ART2 NN (adaptive resonance theory 2 neural network) for fault isolation. From the computer simulation results, it is verified that the proposed fault diagonal method can be performed successfully to detect and isolate faults in a nonlinear system.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.12
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• pp.1498-1506
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• 1999

In this paper, a hybridization of Evolutionary Strategy (ES) and a Two-Phase Neural Network(TPNN) is applied to the optimal environmental and economic operation. As the evolutionary computation, ES is to search for the global optimum based on natural selection and genetics but it shows a defect of reducing the convergence rate in the latter part of search, and often does not search the exact solution. Also, neural network theory as a local search technique can be used to search a more exact solution. But it also has the defect that a solution frequently sticks to the local region. So, new algorithm is presented as hybrid methods by combining merits of two methods. The hybrid algorithm has been tested on Emission Constrained Economic Dispatch (ECED) problem and Weighted Emission Economic Dispatch (WEED) problem for optimal environmental and economic operation. The result indicated that the hybrid approach can outperform the other computational efficiency and accuracy.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.173.4-173
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• 2001

This paper presents a stable nonlinear adaptive control for AUV(Autonomous Underwater Vehicle) by using neural network. AUV's dynamics are highly nonlinear, and their hydrodynamic coefficients vary with different operational conditions. In this paper, the nonlinear uncertainties of the AUV's dynamics are approximated by using LPNN(Linearly parameterized Neural Network). The presented controller is consist of three parallel terms; linear feedback control, sliding mode control, and adaptive control(LPNN). Lyapunov theory is used to guarantee the stability of tracking errors and neural network´s weights errors. Numerical simulations for nonlinear control of the AUV show the effectiveness of the proposed techniques.