• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 춘계학술대회 학술발표 논문집
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• pp.114-117
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• 2000

In this paper, Fuzzy Self-organizing Neural Networks(FSONN) based on Fuzzy Neural Networks(FNN) is proposed to overcome some problems, such as the conflict between ovefitting and good generation, and low reliability. The proposed FSONN consists of FNN and SONN. Here, FNN is used as the premise part of FSONN and SONN is the consequnt part of FSONN. The FUN plays the preceding role of FSONN. For the fuzzy reasoning and learning method in FNN, Simplified fuzzy reasoning and backpropagation learning rule are utilized. The number of layers and the number of nodes in each layers of SONN that is based on the GMDH method are not predetermined, unlike in the case of the popular multi layer perceptron structure and can be generated. Also the partial descriptions of nodes can use various forms such as linear, modified quadratic, cubic, high-order polynomial and so on. In this paper, the optimal design procedure of the proposed FSONN is shown in each step and performance index related to approximation and generalization capabilities of model is evaluated and also discussed.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.1958-1960
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• 2001

In this paper, we propose the design methodology of target tracking system using the identification of TS fuzzy model based on genetic algorithm(GA) and RLS algorithm. In general, the objective of target tracking is to estimate the future trajectory of the target based on the past position of the target obtained from the sensor. In the conventional and mathematical nonlinear filtering method such as extended Kalman filter(EKF), the performance of the system may be deteriorated in highly nonlinear situation. In this paper, to resolve these problems of nonlinear filtering technique, the error of EKF by nonlinearity is compensated by identifying TS fuzzy model. In the proposed method, after composing training datum from the parameters of EKF, by identifying the premise and consequent parameters and the rule numbers of TS fuzzy model using GA, and by tuning finely the consequent parameters of TS fuzzy model using recursive least square(RLS) algorithm, the error of EKF is compensated. Finally, the proposed method is applied to three dimensional tracking problem, and the simulation results shows that the tracking performance is improved by the proposed method.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 추계학술대회 논문집
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• pp.439-443
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• 1996

A fuzzy teaming controller is experimentally designed to control the ball k beam system in this paper. Although most fuzzy controllers have been built just to emulate human decision-making behavior, it is necessary to construct the rule bases by using a learning method with self-improvement when it is difficult or impossible to get them only by expert's experience. The algorithm introduces a reference model to generate a desired output and minimizes a performance index function based on the error and error-rate using the gradient-decent method. In our balancing experiment of the ball ＆ beam system, this paper shows that the fuzzy control rules by learning are superior to the expert's experience.

• 전기학회논문지P
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• 제54권1호
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• pp.8-14
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• 2005

This paper is proposed hybrid artificial intelligent(HAI) controller for speed control of surface permanent magnet synchronous motor(SPMSM) drive. The design of this algorithm based on HAI controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the HAI controller is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2000년도 학술대회논문집
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• pp.193-198
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• 2000

Switched reluctance motor drives have been finding their applications in the variable speed drives due to their relatively low cost, simple and robust structure, controllability and high efficiency. Fuzzy control does not need any model of plant. It is based on plant operator experience and heuristics. Fuzzy control is basically adaptive and gives robust performance for plant parameter variation. This paper deals with the sped control of switched reluctance motor using fuzzy controller with 7-rule based fuzzy logic. The proposed fuzzy controller is superior to the control performance of the conventional PI controller. The fuzzy controller is implemented by 80C196KC, 16 bit one-chip microcontroller.

• 전기학회논문지
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• 제59권5호
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• pp.981-989
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• 2010

In this study, we introduce a new architecture of fuzzy inference system. In the fuzzy inference system, we use Fuzzy C-Means clustering algorithm to form the premise part of the rules. The membership functions standing in the premise part of fuzzy rules do not assume any explicit functional forms, but for any input the resulting activation levels of such radial basis functions directly depend upon the distance between data points by means of the Fuzzy C-Means clustering. As the consequent part of fuzzy rules of the fuzzy inference system (being the local model representing input output relation in the corresponding sub-space), four types of polynomial are considered, namely constant, linear, quadratic and modified quadratic. This offers a significant level of design flexibility as each rule could come with a different type of the local model in its consequence. Either the Least Square Estimator (LSE) or the weighted Least Square Estimator (WLSE)-based learning is exploited to estimate the coefficients of the consequent polynomial of fuzzy rules. In fuzzy modeling, complexity and interpretability (or simplicity) as well as accuracy of the obtained model are essential design criteria. The performance of the fuzzy inference system is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules(clusters) and the order of polynomial in the consequent part of the rules. Accordingly we can obtain preferred model structure through an adjustment of such parameters of the fuzzy inference system. Moreover the comparative experimental study between WLSE and LSE is analyzed according to the change of the number of clusters(rules) as well as polynomial type. The superiority of the proposed model is illustrated and also demonstrated with the use of Automobile Miles per Gallon(MPG), Boston housing called Machine Learning dataset, and Mackey-glass time series dataset.

• 한국방재학회 논문집
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• 제4권1호
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• pp.33-40
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• 2004

본 연구에서는 치수방재 효과를 향상시키기 위한 단일댐운영 모형을 개발하였으며, 제어기법은 퍼지제어 기법을 사용하였다. 본 모형은 저수지 수위와 유입량을 기준으로 제어규칙을 설정하였으며 방류량을 결정하는 제어기준에 따라 Fuzzy I, II, III의 세가지 모형을 개발하였다. Fuzzy I 모형은 6개의 제어규칙에 의해 홍수조절만을 고려한 것이고, Fuzzy II 모형은 I 모형의 치수효과를 가지면서도 홍수후의 저수위를 상승시켜 이수적인 효과도 얻기 위한 모형이며, Fuzzy III 모형은 적응제어모형으로 제어규칙을 9개로 세분화하여 치수효과와 이수효과를 동시에 거둘 수 있도록 한 모형이다.

• Structural Engineering and Mechanics
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• 제56권2호
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• pp.201-221
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• 2015

The fatigue damage problems are frequently encountered in the design of civil engineering structures. A realistic and accurate fatigue life prediction is quite essential to ensure the safety of engineering design. However, constructing a reliable fatigue life prediction model can be quite challenging. The use of traditional deterministic approach in predicting the fatigue life is sometimes too dangerous in the real practical designs as the method itself contains a wide range of uncertain factors. In this paper, a new fatigue life prediction method is going to be proposed where the residual strength is been utilized. Several cumulative damage models, capable of predicting the fatigue life of a structural element, are considered. Based on Miner's rule, a randomized approach is developed from a deterministic equation. The residual strength is used in a one to one transformation methodology which is used for the derivation of the fatigue life. To arrive at more robust results, fuzzy sets are introduced to model the parameter uncertainties. This leads to a convoluted fuzzy based fatigue life prediction model. The developed model is illustrated in an example analysis. The calculated results are compared with real experimental data. The applicability of this approach for a required reliability level is also discussed.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 1999년도 학회창립 10주년 기념 1999년도 가을 학술발표회 논문집
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• pp.495-498
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• 1999

This paper examines a diagnostic model based on the concept of rule and fuzzy pattern recognition. One example is presented to demonstrate the feasibility of the model in diagnosing crack formations in reinforced concrete structures and the result by the expert system is generally satisfactory

• 전기학회논문지
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• 제56권8호
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• pp.1405-1411
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• 2007

In this paper, a new speed sensorless control based on a fuzzy compensator are proposed for the interior permanent magnet synchronous motor (IPMSM) drives. The conventional proportional plus integrate(PI) control are very sensitive to step change of the command speed, parameter variations and load disturbance. To cope with these problems of the PI control, the estimated speeds are compensated by using the fuzzy logic controller (FLC). In the FLC used by the speed compensator of the IPMSM, the system control parameters are adjusted by the fuzzy rule based system, which is a logical model of the human behavior for process control. The effectiveness of algorithm is confirmed by the experiments.