• Title/Summary/Keyword: Neuro Design

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Design of Neuro-Fuzzy Controller using Relative Gain Matrix (상대이득행렬을 이용한 뉴로 퍼지 제어기의 설계)

  • 서삼준;김동식
    • 제어로봇시스템학회:학술대회논문집
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    • 2000.10a
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    • pp.157-157
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    • 2000
  • In the fuzzy control for the multi-variable system, it is difficult to obtain the fuzzy rule. Therefore, the parallel structure of the independent single input-single output fuzzy controller using a pairing between the input and output variable is applied to the multi-variable system. The concept of relative gain matrix is used to obtain the input-output pairs. However, among the input/output variables which are not paired the interactive effects should be taken into account. these mutual coupling of variables affect the control performance. Therefore, for the control system with a strong coupling property, the control performance is sometimes lowered. In this paper, the effect of mutual coupling of variables is considered by tile introduction of a simple compensator. This compensator adjusts the degree of coupling between variables using a neural network. In this proposed neuro-fuzzy controller, the Neural network which is realized by back-propagation algorithm, adjusts the mutual coupling weight between variables.

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The Design of Neuro Controlled Active Suspension (신경회로망을 이용한 능동형 현가장치 제어기 설계)

  • 오정철;김영배
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 1994.10a
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    • pp.414-419
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    • 1994
  • In recent years, there has been an increasing intest in control of active automotive suspension systems with a goal of improving the ride comfort and safety. Many approaches for these purposes have used linearized models of the suspension's dynamics, allowing the use of linear control theory. However, the linearized model does not well descriibe the actual system behavior which is inherently nonlinear. The object of this study is to develop a neuro controlled active suspension for the ride quality improvement. After obtaining active control law using optimal control theory, we use the artificial neural network to train the neuro controller to learn the relation of road input and control force. Form the numerical results, we found that back propagation learning does show good pattern matching and vertical acceleration of the driver's seat and sprung mass.

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Double Gate MOSFET Modeling Based on Adaptive Neuro-Fuzzy Inference System for Nanoscale Circuit Simulation

  • Hayati, Mohsen;Seifi, Majid;Rezaei, Abbas
    • ETRI Journal
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    • v.32 no.4
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    • pp.530-539
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    • 2010
  • As the conventional silicon metal-oxide-semiconductor field-effect transistor (MOSFET) approaches its scaling limits, quantum mechanical effects are expected to become more and more important. Accurate quantum transport simulators are required to explore the essential device physics as a design aid. However, because of the complexity of the analysis, it has been necessary to simulate the quantum mechanical model with high speed and accuracy. In this paper, the modeling of double gate MOSFET based on an adaptive neuro-fuzzy inference system (ANFIS) is presented. The ANFIS model reduces the computational time while keeping the accuracy of physics-based models, like non-equilibrium Green's function formalism. Finally, we import the ANFIS model into the circuit simulator software as a subcircuit. The results show that the compact model based on ANFIS is an efficient tool for the simulation of nanoscale circuits.

Control of Magnetic Flywheel System by Neuro-Fuzzy Logic (뉴로-퍼지를 이용한 플라이휠 제어에 관한 연구)

  • Yang Won-Seok;Kim Young-Bae
    • Journal of the Korean Society for Precision Engineering
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    • v.22 no.6 s.171
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    • pp.90-97
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    • 2005
  • Magnetic flywheel system utilizes a magnetic bearing, which is able to support the shaft without mechanical contacts, and also it is able to control rotational vibration. Magnetic flywheel system is composed of position sensors, a digital controller, actuating amplifiers, an electromagnet and a flywheel. This work applies the neuro-fuzzy control algorithm to control the vibration of a magnetic flywheel system. It proposes the design skill of an optimal controller when the system has structured uncertainty and unstructured uncertainty, i.e. it has a difficulty in extracting the exact mathematical model. Inhibitory action of vibration was verified at the specified rotating speed. Unbalance response, a serious problem in rotating machinery, is improved by using a magnetic bearing with neuro-fuzzy algorithm.

Development of an Adaptive Neuro-Fuzzy Techniques based PD-Model for the Insulation Condition Monitoring and Diagnosis

  • Kim, Y.J.;Lim, J.S.;Park, D.H.;Cho, K.B.
    • Electrical & Electronic Materials
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    • v.11 no.11
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    • pp.1-8
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    • 1998
  • This paper presents an arificial neuro-fuzzy technique based prtial discharge (PD) pattern classifier to power system application. This may require a complicated analysis method employ -ing an experts system due to very complex progressing discharge form under exter-nal stress. After referring briefly to the developments of artificical neural network based PD measurements, the paper outlines how the introduction of new emerging technology has resulted in the design of a number of PD diagnostic systems for practical applicaton of residual lifetime prediction. The appropriate PD data base structure and selection of learning data size of PD pattern based on fractal dimentsional and 3-D PD-normalization, extraction of relevant characteristic fea-ture of PD recognition are discussed. Some practical aspects encountered with unknown stress in the neuro-fuzzy techniques based real time PD recognition are also addressed.

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Neuro-Fuzzy Controller Design for Level Controls

  • Intajag, S.;Tipsuwanporn, V.;Koetsam-ang, N.;Witheephanich, K.
    • 제어로봇시스템학회:학술대회논문집
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    • 2004.08a
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    • pp.546-551
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    • 2004
  • In this paper, a level controller is designed with the neuro-fuzzy model based on Takagi-Sugeno fuzzy system. The fuzzy system is employed as the controller, which can be tuned by the neural network mechanism based on a gradient descent technique. The tuning mechanism will provide an optimal process input by forcing the process error to zero. The proposed controller provides the online tunable mode to adjust the consequent membership function parameters. The controller is implemented with M-file and graphic user interface (GUI) of Matlab program. The program uses MPIBM3 interface card to connect with the industrial processes In the experimentation, the proposed method is tested to vary of the process parameters, set points and load disturbance. Processes of one tank and two tanks are used to evaluate the efficiency of our controller. The results of the both processes are compared with two PID systems that are 3G25A-PIDO1-E and E5AK of OMRON. From the comparison results, our controller performance can be archived in the case of more robustness than the two PID systems.

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Neuro-Fuzzy System for Predicting Optimal Weld Parameters of Horizontal Fillet welds

  • Moon, H.S.;Na, S.J.
    • International Journal of Korean Welding Society
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    • v.1 no.2
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    • pp.36-44
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    • 2001
  • To get the appropriate welding process variables, mathematical modeling in conjunction with many experiments is necessary to predict the magnitude of weld bead shape. Even though the experimental results are reliable, it has a difficulty in accurately predicting welding process variables for the desired weld bead shape because of nonlinear and complex characteristics of welding processes. The welding condition determined for the desired weld bead shape may cause the weld defect if the welding current/voltage/speed combination is improperly selected. In this study, the $2^{n-1}$ fractional factorial design method and correlation parameter were used to investigate the effect of the welding process variables on the fillet joint shape, and the multiple non-linear regression analysis was used for modeling the gas metal arc welding(GMAW)parameters of the fillet joint. Finally, a fuzzy rule-based method and a neural network method were proposed so that the complexity and non-linearity of arc welding phenomena could be effectively overcome. The performance of the proposed neuro-fuzzy system was evaluated through various experiments. The experimental results showed that the proposed neuro-fuzzy system could effectively check the welding conditions as to whether or not weld defects would occur, and also adjust the welding conditions to avoid these weld defects.

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A design of neuro-fuzzy adaptive controller using a reference model following function (기준 모델 추종 기능을 이용한 뉴로-퍼지 적응 제어기 설계)

  • Lee, Young-Seog;Ryoo, Dong-Wan;Seo, Bo-Hyeok
    • Journal of Institute of Control, Robotics and Systems
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    • v.4 no.2
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    • pp.203-208
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    • 1998
  • This paper presents an adaptive fuzzy controller using an neural network and adaptation algorithm. Reference-model following neuro-fuzzy controller(RMFNFC) is invesgated in order to overcome the difficulty of rule selecting and defects of the membership function in the general fuzzy logic controller(FLC). RMFNFC is developed to tune various parameter of the fuzzy controller which is used for the discrete nonlinear system control. RMFNFC is trained with the identification information and control closed loop error. A closed loop error is used for design criteria of a fuzzy controller which characterizes and quantize the control performance required in the overall control system. A control system is trained up the controller with the variation of the system obtained from the identifier and closed loop error. Numerical examples are presented to control of the discrete nonlinear system. Simulation results show the effectiveness of the proposed controller.

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CMAC Neuro-Fuzzy Design for Color Calibration (컬러재현을 위한 CMAC의 뉴로퍼지 설계)

  • 이철희;변오성;문성룡;임기영
    • Journal of the Korean Institute of Intelligent Systems
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    • v.11 no.4
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    • pp.331-335
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    • 2001
  • Cl\iAC model was proposed by Albus [6J to formulate the processing characteristics of the human cerebellum. Instead of the global weight updating scheme used in the back propagation, CMAC use the local weight updating scheme. Therefore, CMAC have the advantage of fast learning and high convergence rate. In this paper, simulate Color Calibration by CMAC in color images and design hardware by VHDL-base high-level synthesis.

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Fuzzy Modeling and Design of Fuzzy Controller Using Fuzzy Clustering (퍼지 클러스터링을 이용한 퍼지 모델링과 퍼지 제어기의 설계)

  • Kwag, Keun-Chang;Park, Sang-Min;Ryu, Jeong-Woong
    • Proceedings of the KIEE Conference
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    • 1997.07b
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    • pp.675-678
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    • 1997
  • In this paper, we present a fast and robust algorithm for the design of fuzzy controller and identifying fuzzy model from numerical data by combining the cluster estimation method with a linear least squares estimation procedure. The proposed method is compared with Adaptive Neuro-Fuzzy Inference System(ANFIS) as the standard example of neuro-fuzzy model. Finally we will show its usefulness and effectiveness for the design of fuzzy controller of a cart-pole system and fuzzy modeling for the coagulant dosing of a water purification system.

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