• Proceedings of the KIPE Conference
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• 1998.07a
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• pp.197-199
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• 1998

A neuro-fuzzy based torque profile model of SRM with considerably improved accuracy is obtained using the measured data for training. The inferred torque profiles, which comprise magnetic non-linearities, represent the dynamic model of SRM. Then the reference torque signal with optimized waveform and switching angle are decided to control the torque directly. Hence, the presented scheme controls the torque in an instantaneous basis, allowing powerful torque control with minimum torque ripple even during the transient operation of the motor. Simulation and experimental results demonstrating the effectiveness of the proposed torque control scheme are presented.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.97-100
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• 2001

CMAC model was proposed by Albus [6] to formulate the processing characteristics of the human cerebellum. Instead of the global weight updating scheme used in the back propagation, CMAC use the local weight updating scheme. Therefore, CMAC have the advantage of fast learning and high convergence rate. In this paper, simulate Color Calibration by CMAC in color images and design hardware by VHDL-base high-level synthesis.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.193-196
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• 2006

프로펠러 회전면에서의 반류분포는 주로 모형시험에 의해서 규명되어 왔다. 이렇게 축적된 데이터베이스를 통해 선박의 기하학적 형상정보와 반류분포 사이의 입출력관계를 모델링할 수 있다. 면 선박 초기설계시 유사선종의 설계에 도움이 된다. 본 연구에서는 이들 입출력 사이의 관계를 뉴로퍼지시스템으로 모델링하고 학습한 후 새로운 입력에 대한 출력값의 검토를 통해 그 유용성을 확인한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.8
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• pp.754-759
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• 2001

In this paper, we propose a TSK(Takagi-Sugeno-Kang)-type fuzzy classifier using PCA(Principal Component Analysis), FCM(Fuzzy c-Means) clustering, ANFIS(Adaptive Neuro-Fuzzy Inference System) and hybrid GA(Genetic Algorithm). First, input data is transformed to reduce correlation among the data components by PCA. FCM clustering is applied to obtain a initial TSK-type fuzzy classifier. Parameter identification is performed by AGA(Adaptive GA) and RLSE(Recursive Least Square Estimate). Finally, we applied the proposed method to Iris data classificationl problems and obtained a better performance than previous works.

• Journal of the Korean Geotechnical Society
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• v.24 no.10
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• pp.57-70
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• 2008

The advantage of piezocone penetration test is a guarantee of continuous data, which is a source of reliable interpretation of target soil layer. Many researches have been carried out f3r several decades and several classification charts have been developed to classify in-situ soil from the cone penetration test result. Since most present classification charts or methods were developed based on the data which were compiled over the world except Korea, they should be verified to be feasible for Korean soil. Furthermore, sometimes their charts provide different soil classification results according to the different input parameters. However, unfortunately, revision of those charts is quite difficult or almost impossible. In this research a new soil classification model is proposed by using fuzzy C-mean clustering and neuro-fuzzy theory based on the 5371 CPT results and soil logging results compiled from 17 local sites around Korea. Proposed neuro-fuzzy soil classification model was verified by comparing the classification results f3r new data, which were not used during learning process of neuro-fuzzy model, with real soil log. Efficiency of proposed neuro-fuzzy model was compared with other soft computing classification models and Robertson method for new data.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.58-69
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• 1998

This paper introduces a novel neuro-fuzzy system based on the polynomial fuzzy neural network(PFNN) architecture. The PFNN consists of a set of if-then rules with appropriate membership functions whose parameters are optimized via a hybrid genetic algorithm. A polynomial neural network is employed in the defuzzification scheme to improve output performance and to select appropriate rules. A performance criterion for model selection, based on the Group Method of DAta Handling is defined to overcome the overfitting problem in the modeling procedure. The hybrid genetic optimization method, which combines a genetic algorithm and the Simplex method, is developed to increase performance even if the length of a chromosome is reduced. A novel coding scheme is presented to describe fuzzy systems for a dynamic search rang in th GA. For a performance assessment of the PFNN inference system, three well-known problems are used for comparison with other methods. The results of these comparisons show that the PFNN inference system outperforms the other methods while it exhibits exceptional robustness characteristics.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.31-36
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• 2001

We design the intelligent diagnosis system for deciding on operation state of TMS Analyzer in this paper. We propose the method to model the neno-fuzzy model for diagnosing the operation state of analyzer by using input and output signals of TMS to measure NOx and SOx. By using experiment data, neuro-fuzzy model is investigated. Validity of the proposed system is asserted by numerical simulation.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.578-580
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• 1998

The paper presents an approach to the structure identification based on the CART (Classification And Regression Tree) algorithm and to the parameter identification by hybrid learning method in neuro-fuzzy system. By using the CART algorithm, the proposed method can roughly estimate the numbers of membership function and fuzzy rule using the centers of decision regions. Then the parameter identification is carried out by the hybrid learning scheme using BP (Back-propagation) and RLSE (Recursive Least Square Estimation) from the numerical data. Finally, we will show it's usefulness for fuzzy modeling to truck backer upper control.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.4
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• pp.885-893
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• 2000

The use of neural-fuzzy system to model mesh seam welding is described in this paper. Conventional, automated process generally involves sophisticated sensing and control techniques applied to various processing parameters. Welding parameters affecting quality include the arc voltage, the welding current, torch travel speed and the pressure and so on. The relationship between the welding parameters and weld quality is not a direct one, and in addition, the effect of the weld parameter variables are not independent of the each other. The effectiveness of the proposed neuro-fuzzy algorithms is demonstrated by computer simulations.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.199-207
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• 2023

Trend Impact Analysis is a prominent hybrid method has been used in future studies with a modified surprise- free forecast. It considers experts' perceptions about how future events may change the surprise-free forecast. It is an advanced forecasting tool used in futures studies for identifying, understanding and analyzing the consequences of unprecedented events on future trends. In this paper, we propose an advanced mechanism to generate more justifiable estimates to the probability of occurrence of an unprecedented event as a function of time with different degrees of severity using adaptive neuro-fuzzy inference system (ANFIS). The key idea of the paper is to enhance the generic process of reasoning with fuzzy logic and neural network by adding the additional step of attributes simulation, as unprecedented events do not occur all of a sudden but rather their occurrence is affected by change in the values of a set of attributes. An ANFIS approach is used to identify the occurrence and severity of an event, depending on the values of its trigger attributes.