• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.881-886
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• 1993

Fuzzy logic rule-based controller has many desirable advantages, which are simple to implement on the real time and need not the information of structure and dynamic characteristics of the system. Thus, nowadays, the scope of the application of the fuzzy logic controller becomes enlarged. But, if the controlled plant is a time-varying and nonlinear system, it is not easy to construct the fuzzy logic rules which usually need the knowledge of an expert. In this paper, an approach in which the logic control rules can be self-organized using genetic algorithm will be proposed and the effectiveness of the proposed method will be verified by computer simulation of the 2 d.o.f. planar robot manipulator.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1248-1253
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• 2009

Although there have been many efforts to reduce accidents on construction sites, such accidents continue to occur. New technologies have recently been developed to improve safety and their performance needs to be evaluated to determine their suitability prior to the application. The assessment for safety performance mainly is conducted depending on qualitative and subjective judgment of supervisors. However, there are rarely proper approaches to assess such qualitative measures. Therefore, we propose a fuzzy-based approach to assessing the performance of a new technology. The assessment of a new technology, called a mobile detector (MD), was carried out as a case study. The output is compared with those by a numerical simulation. As a result, the fuzzy-based performance assessment is shown to be appropriate for this evaluation.

• 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.

• KCID journal
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• v.1 no.1
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• pp.68-73
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• 1994

• Proceedings of the Korean Society for Rock Mechanics Conference
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• 2001.10a
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• pp.143-150
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• 2001

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.69.1-69
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• 2002

1. Introduction 2. STS Cold Rolling Process 3. Shape Control Method 4. On Line Test 5. Conclusions

• Proceedings of the Korean Nuclear Society Conference
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• 1998.10a
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• pp.192-192
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• 1998

• Journal of Korea Water Resources Association
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• v.44 no.7
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• pp.537-551
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• 2011

Based on optimal input data combination selected in the earlier study, Neuro-Fuzzy flood forecasting model linked Takagi-Sugeno fuzzy inference theory with neural network in Wangsukcheon and Gabcheon is established. The established model was applied to Wangsukcheon and Gabcheon and water levels for lead time of 0.5 hr, 1 hr, 1.5 hr, 2.0 hr, 2.5 hr, 3.0 hr are forecasted. For the verification of the model, the comparisons between forecasting floods and observation data are presented. The forecasted results have shown good agreements with observed data. Additionally to evaluate quantitatively for applicability of the model, various statistical errors such as Root Mean Square Error are calculated. As a result of the flood forecasting can be simulated successfully without large errors in all statistical error. This study can greatly contribute to the construction of a high accuracy flood information system that secure lead time in medium and small streams.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.5
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• pp.830-836
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• 2006

In this paper described an auto tuning neuro-fuzzy PID controller based neural network. The PID type controller has been widely used in industrial application due to its simply control structure, easy of design, and inexpensive cost. However, control performance of the PID type controller suffers greatly from high uncertainty and nonlinearity of the system, large disturbances and so on. In this paper will design to take advantage of neural network fuzzy theory and pid controller auto toning technique. The value of initial scaling factors of the proposed controller were determined on the basis of the conventional PID controller parameters tuning methods and then they were adjusted by using neural network control techniques. This controller simple structure and computational complexity are less, and also application is easy and performance is excellent in system that is strong and has nonlinearity to system dynamic behaviour change or disturbance. Finally, the proposed auto tuning neuro-fuzzy controller is applied to magnetic levitation. Simulation results demonstrated that the control performance of the proposed controller is better than that of the conventional controller.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.406-412
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• 2001

In this paper, a new fuzzy single layer learning algorithm is proposed, which shows improved learning time and convergence property than that of the conventional fuzzy single layer perceptron algorithms. First, we investigate the structure of physiological neurons of the nervous system and propose new neuron structures based on fuzzy logic. And by using the proposed fuzzy neuron structures, the model and learning algorithm of Physiological Fuzzy Single Layer Perceptron(P-FSLP) are proposed. For the evaluation of performance of the P-FSLP algorithm, we applied the conventional fuzzy single layer perceptron algorithms and the P-FSLP algorithm to three experiments including Exclusive OR problem, the 3-bit parity bit problem and the recognition of car licence plates, which is an application of image recognition, and evaluated the performance of the algorithms. The experimentation results showed that the proposed P-FSLP algorithm reduces the possibility of local minima more than the conventional fuzzy single layer perceptrons do, and enhances the time and convergence for learning. Furthermore, we found that the P-FSLP algorithm has the great capability for image recognition applications.