• 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.

• 제어로봇시스템학회논문지
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• 제7권8호
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• pp.647-655
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• 2001

In adaptive neuro-control, neural networks are used to approximate unknown plant nonlinearities. Until now, most of the studies in the field of controller design for nonlinear system using neural network considers the affine system with fixed number of neurons. This paper considers nonaffine nonlinear systems and on-line variation of the number of neurons. A control law and adaptive laws for neural network weights are established so that the whole system is stable in the sense of Lyapunov. In addition, at the expense of th input, tracking error converges to the arbitrary small neighborhood of the origin. The efficiency of the proposed scheme is shown through simulations ofa simple nonaffine nonlinear system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.261-264
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• 1992

This paper proposes a neuro-fuzzy adaptive controller which includes the procedure of initializing the identification neural network(INN) and that of learning the control neural network(CNN). The identification neural network is initialized with the informations of the plant which are obtained by a fuzzy controller and the control neural network is trained by the weight informations of the identification neural network during on-line operation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.1073-1075
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• 1996

This paper presents a neuro fuzzy system(NFS) for implementing fuzzy inference system with a monotonic membership function. The modeling and control of a discrete nonlinear system using a NFS is described. The membership function parameters of a identifier and controller are adjusted by back-propagation algorithm. These identifier and controller is constructed to proposed NFS. A on-line identification and control are accomplished by this NFS. A controller is gived information of the system, that is variation of the system output according to that of the control input by a identifier. A controller makes control input in order to control discrete-time nonlinear system. A Simulation is presented to demonstrate the efficiency of a suggested method.

• 한국수자원학회논문집
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• 제37권6호
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• pp.449-460
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• 2004

본 논문에서는 월 댐유입량을 예측하는데 있어서 기상예보정보를 활용한 뉴로-퍼지 시스템의 적용성을 검토하였다. 뉴로-퍼지 알고리즘으로 퍼지이론과 신경망이론의 결합형태인 ANFIS(Adaptive Neuro-Fuzzy Inference System)을 이용하여 모형을 구성하였다. ANFIS의 공간분할에 의한 제어규칙의 선정에 있어 퍼지변수가 증가함에 따라 제어규칙이 기하급수적으로 증가하는 단점을 해결하기 위해 퍼지 클러스터링(Fuzzy Clustering)방법 중 하나인 차감 클러스터링(Subtractive Clustering)을 사용하였다. 또한 본 연구에서는 정성적인 기상예보정보를 정량화 시키는 방법을 제안하였다. AMFIS를 이용하여 월 댐유입량 예측 시, 관측자료만으로 구성된 모형에 의한 예측결과와 관측자료에 기상예보정보를 더하여 구성된 모형에 의한 예측결과를 비교하였다. 그 결과 ANFIS는 기상예보정보를 활용하여 댐유입량을 예측했을 때가 관측자료만으로 예측했을 때보다 예측능력이 더욱 정확함을 보였다.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2005년도 학술대회 논문집
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• pp.315-320
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• 2005

This paper is proposed adaptive fuzzy-neuro controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network 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 nor measured between the motor speed and output of a reference model. The control performance of the adaptive fuzy-neuro controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• 한국안전학회지
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• 제37권1호
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• pp.78-87
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• 2022

In the risk assessment of urban railway systems, a hazard log is created by identifying hazards from accident and failure data. Then, based on a risk matrix, evaluators analyze the frequency and severity of the occurrence of the hazards, conduct the risk assessment, and then establish safety measures for the risk factors prior to risk control. However, because subjective judgments based on the evaluators' experiences affect the risk assessment results, a more objective and automated risk assessment system must be established. In this study, we propose a risk assessment model in which an adaptive neuro-fuzzy inference system (ANFIS), which is combined in artificial neural networks (ANN) and fuzzy inference system (FIS), is applied to the risk assessment of urban railway systems. The newly proposed model is more objective and automated, alleviating the limitations of risk assessments that use a risk matrix. In addition, the reliability of the model was verified by comparing the risk assessment results and risk control priorities between the newly proposed ANFIS-based risk assessment model and the risk assessment using a risk matrix. Results of the comparison indicate that a high level of accuracy was demonstrated in the risk assessment results of the proposed model, and uncertainty and subjectivity were mitigated in the risk control priority.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 A
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• pp.301-303
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• 2002

The load frequency control of power system is one of important subjects in view of system operation and control. To converge within allowance load variation value the frequency and tie-line power flow deviation of each areas, we should regulate the active power output of power plant for regulation in system Applying the NFC(Neuro-Fuzzy Controller) to the model of load frequency control of 2-area power system, we prove that the control is superior to the conventional control technique through computer simulation. For verification of robustness, when we consider generator-rate constraint similar to nonlinearities of power system.

• 조명전기설비학회논문지
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• 제26권7호
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• pp.9-15
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• 2012

This paper is proposed the adaptive fuzzy neuro controller(AFNC) for high performance of induction motor drive. The design of this algorithm based on the AFNC that is implemented using fuzzy controller(FC) and neural network(NN). This controller uses fuzzy rule as training patterns of a NN. Also, this controller adjusts the weights between the neurons of NN to minimize the error between the command output and the actual output using the back-propagation method. The control performance of the AFNC is evaluated by analysis in various operating conditions. The results of analysis prove that the proposed control system has high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• Structural Engineering and Mechanics
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• 제62권2호
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• pp.237-246
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• 2017

In the present paper a new Neuro-Wavelet control algorithm is proposed based on a cost function to actively control the vibrations of structures under earthquake loads. A wavelet neural network (WNN) was developed to train the control algorithm. This algorithm is designed to control multi-degree-of-freedom (MDOF) structures which consider the geometric and material non-linearity, structural irregularity, and the incident direction of an earthquake load. The training process of the algorithm was performed by using the El-Centro 1940 earthquake record. A numerical model of a three dimensional (3D) three story building was used to accredit the control algorithm under three different seismic loads. Displacement responses and hysteretic behavior of the structure before and after the application of the controller showed that the proposed strategy can be applied effectively to suppress the structural vibrations.