• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.62-64
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• 1996

Neural network as a controller of a nonlinear system and a system identifier has been studied during the past few years. A well trained neural network identifier can be used as a system predictor. We proposed the method to design multi-step ahead predictor and multi-step predictive controller using neural network. We used the input and out put data of B system to train the NNP and used the forecasted approximat system output from NNP as B input of NNC. In this paper we used two-step ahead predictive controller to test B heating controll system and compared with PI controller.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.401-403
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• 1996

In this paper, a new control strategy is presented that combines sliding mode control theory with a neural network. Sliding mode control theory requires the complete knowledge of the dynamics of the controlled system. However, in practice, one often bas only a small number of state measurements. This could be a serious limitation on the practical usefulness of sliding mode control theory. A multilayer neural network is employed to solve this kind of problem. The neural network serves as a compensator without a prior knowledge about the system. The proposed control algorithm is applied to a class of uncertain nonlinear system. The robustness against parameter uncertainty, nonlinearity and external disturbances, and the effectiveness is verified by the simulation results.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.244-247
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• 1993

In this paper, Fuzzy-Neural Network Control system that has the characteristic of fuzzy control to be controlled easily end the good characteristic of a artificial neural network to control the plant due to its learning is presented. A fuzzy rule to be applied is selected automatically by the allocated neurons. The neurons correspond to Fuzzy rules which ere created by a expert. To adaptivity, the more precise modeling is implemented by error beck-propagation learning of adjusting the link-weight of fuzzy membership function in Fuzzy-Neural Network. The more classified fuzzy rule is used to include the property of Dual Mode Method. To test the effectiveness of the algorithm presented above, the simulation for position end velocity of DC servo motor is implemented.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.131-133
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• 1993

In the power system state estimation, the J(x)-index test and normalized residuals $r_N$ have been used to detect the presence of bad measurements and identify their location. But, these methods require the complete re-estimation of system states whenever bad data is identified. This paper presents back-propagation neural network model using autoregressive filter for identification of bad measurements. The performances of neural network method are compared with those of conventional methods and simulation results show the good performance in the bad data identification based on the neural network under sample power system.

• Applied Microscopy
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• v.46 no.4
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• pp.171-175
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• 2016

A distinctive neuronal network in the brain is believed to make us unique individuals. Electron microscopy is a valuable tool for examining ultrastructural characteristics of neurons, synapses, and subcellular organelles. A recent technological breakthrough in volume electron microscopy allows large-scale circuit reconstruction of the nervous system with unprecedented detail. Serial-section electron microscopy-previously the domain of specialists-became automated with the advent of innovative systems such as the focused ion beam and serial block-face scanning electron microscopes and the automated tape-collecting ultramicrotome. Further advances in microscopic design and instrumentation are also available, which allow the reconstruction of unprecedentedly large volumes of brain tissue at high speed. The recent introduction of correlative light and electron microscopy will help to identify specific neural circuits associated with behavioral characteristics and revolutionize our understanding of how the brain works.

• Journal of Microbiology and Biotechnology
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• v.22 no.7
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• pp.923-929
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• 2012

Optimization of culture conditions for L-asparaginase production by submerged fermentation of Aspergillus terreus MTCC 1782 was studied using a 3-level central composite design of response surface methodology and artificial neural network linked genetic algorithm. The artificial neural network linked genetic algorithm was found to be more efficient than response surface methodology. The experimental $_L$-asparaginase activity of 43.29 IU/ml was obtained at the optimum culture conditions of temperature $35^{\circ}C$, initial pH 6.3, inoculum size 1% (v/v), agitation rate 140 rpm, and incubation time 58.5 h of the artificial neural network linked genetic algorithm, which was close to the predicted activity of 44.38 IU/ml. Characteristics of $_L$-asparaginase production by A. terreus MTCC 1782 were studied in a 3 L bench-scale bioreactor.

• Proceedings of the KIPE Conference
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• 2002.11a
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• pp.36-39
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• 2002

This paper presents a simulation results of sensorless control of Switched Reluctance Motor(SRM) using neural network. The basic algorithm of this scheme is based on the flux linkage characteristic according to the phase current and the rotor position. A sufficient simulation data was used for neural network training. Through measurement of the phase flux linkage and phase currents the neural network is able to estimate the rotor position. The simulation result shows some good results, and possibility of this algorithm.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.4
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• pp.151-157
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• 2003

In this paper, a blackbox approach is carried out to model the nonlinear dynamic bushing model. One-axis durability test is performed to describe the mechanical behavior of typical vehicle elastomeric components. The results of the tests are used to develop an empirical bushing model with an artificial neural network. The back propagation algorithm is used to obtain the weighting factor of the neural network. Since the output for a dynamic system depends on the histories of inputs and outputs, Narendra algorithm of 'NARMAX' form is employed to consider these effects. A numerical example is carried out to verify the developed bushing model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.800-803
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• 2006

In this paper, we propose a method for improving the performance of the face recognition using a hybrid neural network. The propose method focused on improving face recognition technique using SOM and LVQ. In order to verify the effectiveness of the proposed method, we performed simulations on face database supplied ORL. The results show that the proposed method considerably improves on the performance of the eigenface, hidden markov model, multilayer neural network.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1428-1429
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• 2007

The paper used to the Neral Netwok for a forecasting conservation system. A neural network is a powerful data modeling tool that is able to capture and represent complex input/output relationships. The motivation for the development of neural network technology stemmed from the desire to develop an artificial system that could perform "intelligent" tasks similar to those performed by the human brain. The true power and advantage of neural network lies in their ability to represent both linear and non-linear relationships and in their ability to learn these relationships directly from the data being modeled. Form results of this study, the Neral Netwok is will play an important role for insulation diagnosis system of real site GIS and power equipment using Dry-Air gas.