• 제어로봇시스템학회논문지
• /
• 제6권8호
• /
• pp.710-718
• /
• 2000

In this paper we described an approach to automation of visual inspection of solder joint defects of SMC(Surface Mounted Components) on PCBs(Printed Circuit Board) by using neural network and fuzzy rule-based classification method. Inherently the surface of the solder joints is curved tiny and specular reflective it induces difficulty of taking good image of the solder joints. And the shape of the solder joints tends to greatly vary with the soldering condition and the shapes are not identical to each other even though the solder joints belong to a set of the same soldering quality. This problem makes it difficult to classify the solder joints according to their qualities. Neural network and fuzzy rule-based classification method is proposed to effi-ciently make human-like classification criteria of the solder joint shapes. The performance of the proposed approach is tested on numerous samples of commercial computer PCB boards and compared with the results of the human inspector performance and the conventional Kohonen network.

• 한국지능시스템학회논문지
• /
• 제20권5호
• /
• pp.672-676
• /
• 2010

본 논문은 연결강도를 조정할 때 결정 경계선 근처에 있는 데이터를 더 반영하는 학습법칙을 제안하였다. 이 학습법칙은 outlier가 결정 경계선에 미치는 영향을 줄여 더 나은 결정 경계선을 형성하도록 한다. 제안하는 학습법칙을 IAFC(Integrated Adaptive Fuzzy Clustering) 신경회로망의 구조에 적용하였다. IAFC 신경회로망은 배운 것을 유지하는 안정성이 있으면서, 새로운 것을 배울 수 있는 안정성이 있다. 이 퍼지 신경회로망의 성능과 LVQ(Learning Vector Quantization) 신경회로망 및 오류역전파 신경회로망의 성능과 비교하였다. 실험결과 제안하는 퍼지 신경회로망의 성능이 우수함을 보여주었다.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
• /
• pp.134-134
• /
• 2000

A switching control using multiple gains in the fuzzy rule is newly proposed for an abruptly changing hydraulic servo system. The proposed scheme employs fuzzy PID control, where modified input parameters are used, and LVQNN(Learning Vector Quantization Neural Network) as a switching controller (supervisor). Simulation and experimental studies have been carried out to validate and illustrate the proposed controller.

• 전력전자학회논문지
• /
• 제3권3호
• /
• pp.173-183
• /
• 1998

본 논문은 신경회로망에 의한 퍼지제어기의 소속함수를 자동동조하는 방법을 제시하였다. 신경회로망 에뮬레이터는 퍼지제어기의 소속함수와 퍼지규칙을 재구성하는 경로를 제공하며, 재구성된 퍼지제어기는 유도전동기의 속도제어를 위해 사용한다. 따라서, 연산 시간과 시스템 성능의 관점에서 제안된 방법은 전동기 상수가 변동될 시에도 기존의 제어 방식보다 우수하다. 공간전압벡터 PWM 발생을 위한 고속연산을 수행하고 자기학습형 퍼지제어기 알고리즘을 구현하기 위해서 32비트 마이크로프로세서인 DSP(TMS320C31)을 사용하였다. 컴퓨터 시뮬레이션과 실험 결과를 통하여, 제안된 방식이 PI 제어기나 기존의 퍼지제어기보다 향상된 제어 성능을 보일 수 있음을 확인하였다.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1997년도 추계학술대회 논문집 학회본부
• /
• pp.505-508
• /
• 1997

In this paper, an auto-tuning method for fuzzy controller based on the neural network is presented. The backpropagated error of neural emulator offers the path which reforms the fuzzy controller's membership functions and fuzzy rule, and used for speed control of induction motor. For the torque control method, an indirect vector control scheme with slip calculation is used because of its stable characteristics regardless of speed. Motor input current is regulated by a current controlled voltage source PWM inverter using space voltage vector technique. Also, the scheme of current control fuzzy controller is synchronous reference frame with decoupling term. DSP(TMS320C31) is used to achieve the high speed calculation of the space voltage vector PWM and to build the self-learning fuzz. control algorithm. An IPM is used to simplify hardware design.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1992년도 하계학술대회 논문집 B
• /
• pp.1228-1230
• /
• 1992

The permanent magnet synchronous motor(PMSM) is receiving Increased attention for servo drive applications in recent years because of its high torque to inertia ratio, superior power density and high efficiency. By vector-controll method, PMSM has the same operating characterics as seperately excited dc motor. The drive system of servo motor is requested to have an accurate response for the reference input and a quick recovery for the disturbance such as load torque. However, when the unknown disturbances and parameter variations are imposed on the permanent magnet synchronous motor(PMSM), the drive system is significantly effected by them. As a result, the drive system with both a fast compensation and a robustness to a parameter variations is requested. This paper investigates the possibility of applying the fuzzy logic controller(FLC) using Multi-Rule Base In a servo motor control system. In this paper, The five Rule Bases(1 to 5) are selected to recover the state error caused by the disturbance in steady state. In the initial operating mode. Rule Base 0 is used. To show the validity of the proposed fuzzy logic controll system, the computer simulation results are provided.

• 대한전기학회논문지:시스템및제어부문D
• /
• 제55권10호
• /
• pp.443-445
• /
• 2006

Generally PI controller is used to control the speed of an induction motor. It has the good performance of speed control in case of adjusting the control parameters. But it occurred the problem to change the control parameters in the change of operation condition. In order to solve this problem, Fuzzy control or Artificial neural network is introduced in the speed control of an induction motor. However, Fuzzy control have the problems as the difficulties to change the membership function and fuzzy rule and the remaining error Also Neural network has the problem as the difficulties to analyze the behavior of inner part. Therefore, the study on the combination of two controller is proceeded. In this paper, Fuzzy-neural controller to make up these controllers in parallel is proposed and the speed control of an induction motor is performed using the proposed controller Through the experiment, the fast response and good stability of the proposed speed controller is proved.

• 한국정보통신학회논문지
• /
• 제4권3호
• /
• pp.611-618
• /
• 2000

This paper presents a shape classification method of dynamic image based on adaptive fuzzy inference. It describes the design scheme of fuzzy inference algorithm which makes it suitable for low speed systems such as conveyor, uninhabited transportation. In the first Discrete Wavelet Transform(DWT) is utilized to extract the motion vector in a sequential images. This approach provides a mechanism to simple but robust information which is desirable when dealing with an unknown environment. By using feature parameters of moving object, fuzzy if - then rule which can be able to adapt the variation of circumstances is devised. Then applying the implication function, shape classification processes are performed. Experimental results are presented to testify the performance and applicability of the proposed algorithm.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2007년도 제38회 하계학술대회
• /
• pp.1114-1115
• /
• 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.

• 대한전자공학회논문지SP
• /
• 제40권6호
• /
• pp.90-97
• /
• 2003

본 논문은 FSVQ(first section vector quantization)와 퍼지 개념을 이용한 HMM(hidden Markov model)에 기초를 둔 음성인식을 제안한다. 제안된 연구 방법에서는 첫 번째 구간의 코드북(codebook)을 만든 후, 첫 번째 구간의 코드북으로부터, 퍼지 개념을 도입하여 확률값이 큰 순서에 의해 다중 관측열을 구한다. 그 다음, 코드북으로부터 첫 번째 구간의 관측열을 학습시키고 인식할 때에도 같은 개념으로 첫 번째 구간에서의 확률 값이 가장 높은 단어를 인식된 단어로 선택한다. 인식 대상 어휘로는 전철역명을 선택하였으며, 특징 파라메타로는 LPC ？스트럼을 사용하였다. 제안된 방법에 의한 인식 실험을 수행하는 것 이외에도 비교를 위하여 이전에 실험한 몇 가지 방법의 인식 실험을 같은 조건하에서 같은 데이터로 수행한다. 실험 결과, 본 연구에서 제안한 FSVQ와 퍼지 개념을 이용한 HMM에 기초를 둔 방법이 다른 음성 인식방법들보다 인식률이 우수함을 입증하였다.