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Stable Tracking Control to a Non-linear Process Via Neural Network Model
Yujia Zhai 한국융합학회 2014 한국융합학회논문지 Vol.5 No.4
A stable neural network control scheme for unknown non-linear systems is developed in this paper. While the control variable is optimised to minimize the performance index, convergence of the index is guaranteed asymptotically stable by a Lyapnov control law. The optimization is achieved using a gradient descent searching algorithm and is consequently slow. A fast convergence algorithm using an adaptive learning rate is employed to speed up the convergence. Application of the stable control to a single input single output (SISO) non-linear system is simulated. The satisfactory control performance is obtained.
Relation between Certainty and Uncertainty with Fuzzy Entropy and Similarity Measure
Sanghyuk Lee,Yujia Zhai 한국융합학회 2014 한국융합학회논문지 Vol.5 No.4
We survey the relation of fuzzy entropy measure and similarity measure. Each measure represents features of data uncertainty and certainty between comparative data group. With the help of one-to-one correspondence characteristics, distance measure and similarity measure have been expressed by the complementary characteristics. We construct similarity measure using distance measure, and verification of usefulness is proved. Furthermore analysis of similarity measure from fuzzy entropy measure is also discussed.
HW/SW Co-design of a Visual Driver Drowsiness Detection System
Yu, Tian,Zhai, Yujia Convergence Society for SMB 2014 융합정보논문지 Vol.4 No.1
PID 오토 튜닝 컨트롤러는 퍼지 논리를 통해 설계되었다. 이러한 오류 및 오류 파생 의견으로 일반적인 값은 발견적 표현으로 변경, 그들은 퍼지 및 defuzzification 과정을 통해 PID 이득을 결정했다. 퍼지 절차 및 PID 제어기 설계는 개별적으로 간주하고, 그것들을 혼합하고, 분석 하였다. 퍼지 논리에 의해 획득 자동 조정 PID 컨트롤러는 3 차 플랜트 제어 이하의 능력을 보여 주었다. 또한 설계된 자동 동조 방식으로 추적 문제를 참조하는 데 적용한다. PID auto-tuning controller was designed via fuzzy logic. Typical values such as error and error derivative feedback were changed as heuristic expressions, and they determine PID gain through fuzzy logic and defuzzification process. Fuzzy procedure and PID controller design were considered separately, and they are combined and analyzed. Obtained auto-tuning PID controller by Fuzzy Logic showed the ability for less than 3rd order plant control. We also applied to reference tracking problem with the designed auto-tuning scheme.