• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.6
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• pp.616-628
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• 2000

In this paper, we present the modeling of the ozone prediction system using Neuro-Fuzzy approaches. The mechanism of ozone concentration is highly complex, nonlinear, and nonstationary, the modeling of ozone prediction system has many problems and the results of prediction is not a good performance so far. The Dynamic Polynomial Neural Network(DPNN) which employs a typical algorithm of GMDH(Group Method of Data Handling) is a useful method for data analysis, identification of nonlinear complex system, and prediction of a dynamical system. The structure of the final model is compact and the computation speed to produce an output is faster than other modeling methods. In addition to DPNN, this paper also includes a Fuzzy Logic Method for modeling of ozone prediction system. The results of each modeling method and the performance of ozone prediction are presented. The proposed method shows that the prediction to the ozone concentration based upon Neuro-Fuzzy approaches gives us a good performance for ozone prediction in high and low ozone concentration with the ability of superior data approximation and self organization.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.83-86
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• 2001

In this paper, it study about the image filter applied the hybrid fuzzy membership function to the neuro-fuzzy system. Here, this system applys the genetic algorithm in order to obtain the optimal image as the iteration carry for making the data value in the error. It is removed the included noise in an image using the proposed image filter and compared the proposed image filter performance with the other filters using MATLAB. And it is found that the proposed filter performance is superior to the other filters which has the similar structure through the images. To show the superior ability, it is compared with MSE and SNR for images.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2008.10a
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• pp.311-314
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• 2008

This paper is proposed a high performance speed con01 of the Interior Permanent Magnet Synchronous Motor through the Fuzzy-Neuro SV-PWM, SV-PWM is controlled using Fuzzy-Neuro control. SV-PWM can be maximum used maximum dc link voltage and is excellent control method due to characteristic to reducing harmonic more than others. The hybrid combination of fuzzy control and neural network will produce a powerful representation flexibility and numerical processing capability. Simulation results are presented to show the validity of the proposed algorithm.

• Electrical & Electronic Materials
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• v.11 no.11
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• pp.1-8
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• 1998

This paper presents an arificial neuro-fuzzy technique based prtial discharge (PD) pattern classifier to power system application. This may require a complicated analysis method employ -ing an experts system due to very complex progressing discharge form under exter-nal stress. After referring briefly to the developments of artificical neural network based PD measurements, the paper outlines how the introduction of new emerging technology has resulted in the design of a number of PD diagnostic systems for practical applicaton of residual lifetime prediction. The appropriate PD data base structure and selection of learning data size of PD pattern based on fractal dimentsional and 3-D PD-normalization, extraction of relevant characteristic fea-ture of PD recognition are discussed. Some practical aspects encountered with unknown stress in the neuro-fuzzy techniques based real time PD recognition are also addressed.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.928-938
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• 2008

This study presents an approach for approximation an unknown function from a numerical data set based on the synthesis of a neuro-fuzzy model. An adaptive input data space parting method, which is used for building hyperbox-shaped clusters in the input data space, is proposed. Each data cluster is implemented here as a fuzzy set using a membership function MF with a hyperbox core that is constructed from a min vertex and a max vertex. The focus of interest in proposed approach is to increase degree of fit between characteristics of the given numerical data set and the established fuzzy sets used to approximate it. A new cutting procedure, named NCP, is proposed. The NCP is an adaptive cutting procedure using a pure function $\Psi$ and a penalty function $\tau$ for direction the input data space parting process. New algorithms named CSHL, HLM1 and HLM2 are presented. The first new algorithm, CSHL, built based on the cutting procedure NCP, is used to create hyperbox-shaped data clusters. The second and the third algorithm are used to establish adaptive neuro- fuzzy inference systems. A series of numerical experiments are performed to assess the efficiency of the proposed approach.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.4
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• pp.293-301
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• 2001

This paper reviews three methods to compute a pointwise confidence interval of neuro-fuzzy models and compares their estimation perfonnanee through simulations. The eOITl.putation methods under consideration include stacked generalization using cross-validation, predictive error bar in regressive models, and local reliability measure for the networks employing a local representation scheme. These methods implemented on the neuro-fuzzy models are applied to the problems of simple function approximation and chaotic time series prediction. The results of reliability estimation are compared both quantitatively and qualitatively.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.778_779
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• 2009

The maximum output torque developed by the machine is dependent on the allowable current rating and maximum voltage that the inverter can supply to the machine. Therefore, to use the inverter capacity fully, it is desirable to use the control scheme considering the voltage and current limit condition, which can yield the maximum torque per ampere over the entire speed range. The paper is proposed maximum torque control of induction motor drive using adaptive learning neuro fuzzy controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d, q axis current $_i_{ds}$, $i_{qs}$ for maximum torque operation is derived. The proposed control algorithm is applied to induction motor drive system controlled adaptive learning neuro fuzzy controller and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the adaptive learning neuro fuzzy controller and ANN controller.

• Journal of Sensor Science and Technology
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• v.9 no.6
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• pp.440-447
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• 2000

This paper describes a practical system to classify material temperature responses by composition of curve fitting and neuro-fuzzy inference. There are problems with a classification system which utilizes temperature responses. It requires too much time to approach the steady state of temperature response and it has to be filtered to remove the noise which occurs in experiments. Thus, this paper proposes a practical method using curve fitting only for transient state to remove the above problems of time and noise. Using the neuro-fuzzy system, the thermal conductivity of the material can be inferred on various ambient temperatures. So the material can be classified via its inferred thermal conductivity. To realize the system, we designed a contact sensor which has a similar structure with human finger, implemented a hardware system, and developed a classification software of curve fitting and neuro-fuzzy algorithm.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.39 no.1
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• pp.77-83
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• 2003

Autonomous mobile robots based on the Web have been already used in public places such as museums. There are many kinds of problems to be solved because of the limitation of Web and the dynamically changing environment. We present a methodology for intelligent mobile robot that demonstrates a certain degree of autonomy in navigation applications. In this paper, we focus on a mobile robot navigator equipped with neuro-fuzzy controller which perceives the environment, make decisions, and take actions. The neuro-fuzzy controller equipped with collision avoidance behavior and target trace behavior enables the mobile robot to navigate in dynamic environment from the start location to goal location. Most telerobotics system workable on the Web have used standard Internet techniques such as HTTP, CGI and Scripting languages. However, for mobile robot navigations, these tools have significant limitations. In our study, C# and ASP.NET are used for both the client and the server side programs because of their interactivity and quick responsibility. Two kinds of simulations are performed to verify our proposed method. Our approach is verified through computer simulations of collision avoidance and target trace.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.3
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• pp.167-175
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• 2021

Reusing electric vehicle batteries after they have been retired from mobile applications is considered a feasible solution to reduce the demand for new material and electric vehicle costs. However, the evaluation of the value and the performance of second-life batteries remain a problem that should be solved for the successful application of such batteries. The present work aims to estimate the remaining useful life of Li-ion batteries through the neuro-fuzzy system with the equivalent circuit parameters obtained by Electrochemical Impedance Spectroscopy (EIS). To obtain the impedance spectra of the Li-ion battery over the life, a 18650 cylindrical cell has been aged by 1035 charge/discharge cycles. Moreover, the capacity and the parameters of the equivalent circuit of a Li-ion battery have been recorded. Then, the data are used to establish a neuro-fuzzy system to estimate the remaining useful life of the battery. The experimental results show that the developed algorithm can estimate the remaining capacity of the battery with an RMSE error of 0.841%.