• Structural Engineering and Mechanics
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• 제87권3호
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• pp.231-242
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• 2023

A novel combinatorial type-2 adaptive neuro-fuzzy inference system (T2-ANFIS) and robust proportional integral derivative (PID) control framework for intelligent vibration mitigation of uncertain structural system is introduced. The fuzzy logic controllers (FLCs), are designed independently of the mathematical model of the system. The type-1 FLCs, have a limited ability to reduce the effect of uncertainty, due to their fuzzy sets with a crisp degree of membership. In real applications, the consequent part of the fuzzy rules is uncertain. The type-2 FLCs, are robust to the fuzzy rules and the process parameters due to the fuzzy degree of membership functions and footprint of uncertainty (FOU). The adaptivity of the proposed method is provided with the optimum tuning of the parameters using the neural network training algorithms. In our approach, the PID control force is obtained using the generalized type-2 neuro-fuzzy in such a way that the stability and robustness of the controller are guaranteed. The robust performance and stability of the presented framework are demonstrated in a numerical study for an eleven-story seismically-excited building structure combined with an active tuned mass damper (ATMD). The results indicate that the introduced type-2 neuro-fuzzy PID control scheme is effective to attenuate plant states in the presence of the structured and unstructured uncertainties, compared to the conventional, type-1 FLC, type-2 FLC, and type-1 neuro-fuzzy PID controllers.

• 제어로봇시스템학회지
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• 제1권3호
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• pp.109-117
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• 1995

이 글에서는 neuro-fuzzy control system에 관심을 가지신 control engineer들에게 다소나마 보탬이 되고자 neuro-fuzzy control system을 간단히 소개한 후 이 system이 실제로 어떻게 응용되고 있는지 살펴보고자 한다.

• Smart Structures and Systems
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• 제16권6호
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• pp.1107-1132
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• 2015

A damage detection algorithm based on neuro fuzzy hybrid system is presented in this study for location and severity predictions of cracks in beam-like structures. A combination of eigenfrequencies and rotation deviation curves are utilized as input to the soft computing technique. Both single and multiple damage cases are considered. Theoretical expressions leading to modal properties of damaged beam elements are provided. The beam formulation is based on Euler-Bernoulli theory. The cracked section of beam is simulated employing discrete spring model whose compliance is computed from stress intensity factors of fracture mechanics. A hybrid neuro fuzzy technique is utilized to solve the inverse problem of crack identification. Two different neuro fuzzy systems including grid partitioning (GP) and subtractive clustering (SC) are investigated for the highlighted problem. Several error metrics are utilized for evaluating the accuracy of the hybrid algorithms. The study is the first in terms of 1) using the two models of neuro fuzzy systems in crack detection and 2) considering multiple damages in beam elements employing the fused neuro fuzzy procedures. At the end of the study, the developed hybrid models are tested by utilizing the noise-contaminated data. Considering the robustness of the models, they can be employed as damage identification algorithms in health monitoring of beam-like structures.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.387-390
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• 1998

In this paper, we suggested a neuro-fuzzy learning algorithm for tuning fuzzy rules, in which a fuzzy system model is of additive-type. Using the method, it is possible to reduce the computation size, since performing the fuzzy inference and tuning the fuzzy rules of each fuzzy subsystem model are independent. Moreover, the efficiency of suggested method is shown by means of a numerical example.

• 대한교통학회:학술대회논문집
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• 대한교통학회 1999년도 제36회 학술발표회논문집
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• pp.446-451
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• 1999

• 대한기계학회논문집A
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• 제27권2호
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• pp.310-316
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• 2003

One of the significant subject in the automatic arc welding is to establish control system of the welding parameters for controlling bead geometry as a criterion to evaluate the quality of arc welding. This paper proposes an inference algorithm for bead geometry in CMA Welding using Neuro-Fuzzy algorithm. The characteristic welding parameters are measured by the circuit composed of hall sensor, voltage divider tachometer, etc. and then the bead geometry of each weld pool is calculated and detected by an image processing with CCD camera and a measuring with microscope. The relationships between the characteristic welding parameters and the bead geometry have been arranged empirically. From the result of experiments, membership functions and fuzzy rules are tuned and determined by the learning of neural network, and then the relationship between actual bead geometry and inferred bead geometry are concluded by fuzzy logic controller. In the applied inference system of bead geometry using Neuro-Fuzzy algorithm, the inference error percent is within -5%∼+4% in case of bead width, -10%∼+10% in bead height, -5%∼+6% in bead area, -10%∼+10% in penetration. Use of the Neuro-Fuzzy algorithm allows the CMA Welding system to evaluate the quality in bead geometry in real time as the welding parameters change.

• 한국지진공학회논문집
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• 제9권2호통권42호
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• pp.7-15
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• 2005

본 연구에서는 하이브리드 면진장치가 설치된 단자유도 구조물의 동적거동을 예측할 수 있는 수치해석모델을 제안한다. 하이브리드 면진장치는 MR 감쇠기와 마찰진자시스템(FPS)으로 구성된다. MR감쇠기의 동적거동을 모형화하기 위하여 뉴로-퍼지 모델을 사용한다. 다양한 변위, 속도, 전압의 조합을 사용하여 MR 감쇠기의 성능실험을 수행한 후 얻어진 데이터를 이용하여 MR 감쇠기 뉴로-퍼지 모델을 ANFIS로 학습시킨다. FPS의 모형화는 본 연구에서 유도한 비선형 모델식에 근거하여 뉴로-퍼지 모형화방법을 사용하여 이루어진다. 본 연구에서는 MR 감쇠기로 전달되는 제어전압을 조절하기 위하여 퍼지논리제어기를 사용한다. 다양한 지진하중을 사용한 진동대 실험을 통하여 얻은 실험체의 동적응답과와 뉴로-퍼지 모형화방법을 사용한 수치해석의 결과를 비교한다. 뉴로-퍼지 모델을 사용하여 MR 감쇠기와 FPS를 모형화해서 수치해석을 수행한 결과 하이브리드 면진장치의 동적거동을 매우 정확하게 예측할 수 있었다.

• 대한전기학회논문지:전력기술부문A
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• 제54권10호
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• pp.467-474
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• 2005

This paper presents a systematic method to compute a reliability measure for a short term electrical load forecasting system using neuro-fuzzy models. It has been realized that the reliability computation is essential for a load forecasting system to be applied practically. The proposed method employs a local reliability measure in order to exploit the local representation characteristic of the neuro-fuzzy models. It, hence, estimates the reliability of each fuzzy rule learned. The design procedure of the proposed short term load forecasting system is as follows: (1) construct initial structures of neuro-fuzzy models, (2) store them in the initial structure bank, (3) train the neuro-fuzzy model using an appropriate initial structure, and (4) compute load prediction and its reliability. In order to demonstrate the viability of the proposed method, we develop an one hour ahead load forecasting system by using the real load data collected during 1996 and 1997 at KEPCO. Simulation results suggest that the proposed scheme extends the applicability of the load forecasting system with the reliably computed reliability measure.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2004년도 춘계학술대회 학술발표 논문집 제14권 제1호
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• pp.454-457
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• 2004

In this paper, control method proposed for effective speed control of the induction motor indirect vector control. For the induction motor drive, indirect vector control scheme that controls torque current and flux current of the stator current independently so that it can have improved dynamics. Also, neuro-fuzzy algorithm employed for torque current control in order to optimal speed control The proposed neuro-fuzzy algorithm can be applied to the precise speed control of an induction motor drive system or the field of any other power systems.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 하계종합학술대회 논문집(3)
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• pp.173-176
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• 2001

This paper proposes training algorithms of neuro-fuzzy systems. First, we introduce a structure training algorithm, which produces the necessary number of hidden nodes from training data. From this algorithm, initial fuzzy rules are also obtained. Second, the parameter training algorithm using evolution strategy is introduced. In order to show their usefulness, we apply our neuro-fuzzy system to a nonlinear system identification problem. It was found from experiments that proposed training algorithms works well.