• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.6
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• pp.390-395
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• 2005

Sunspots are dark areas that grow and decay on the lowest level of the sun that is visible from the Earth. Shot-term predictions of solar activity are essential to help plan missions and to design satellites that will survive for their useful lifetimes. This paper presents a parallel-structure fuzzy system(PSFS) for prediction of sunspot number time series. The PSFS consists of a multiple number of component fuzzy systems connected in parallel. Each component fuzzy system in the PSFS predicts future data independently based on its past time series data with different embedding dimension and time delay. An embedding dimension determines the number of inputs of each component fuzzy system and a time delay decides the interval of inputs of the time series. According to the embedding dimension and the time delay, the component fuzzy system takes various input-output pairs. The PSFS determines the final predicted value as an average of all the outputs of the component fuzzy systems in order to reduce error accumulation effect.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.2
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• pp.113-121
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• 2000

This paper presents a parallel-structure fuzzy system(PSFS) for prediction of time series data. The PSFS consists of a multiple number of fuzzy systems connected in parallel. Each component fuzzy system in the PSFS predicts the same future data independently based on its past time series data with different embedding dimension and time delay. The component fuzzy systems are characterized by multiple-input singleoutput( MIS0) Sugeno-type fuzzy rules modeled by clustering input-output product space data. The optimal embedding dimension for each component fuzzy system is chosen to have superior prediction performance for a given value of time delay. The PSFS determines the final prediction result by averaging the outputs of all the component fuzzy systems excluding the predicted data with the minimum and the maximum values in order to reduce error accumulation effect.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.201-203
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• 2007

The paper concerns the successive optimization for structure and parameters of fuzzy inference systems that is based on parallel Genetic Algorithms (PGA) and information data granulation (IG). PGA is multi, population based genetic algorithms, and it is used tu optimize structure and parameters of fuzzy model simultaneously, The granulation is realized with the aid of the C-means clustering. The concept of information granulation was applied to the fuzzy model in order to enhance the abilities of structural optimization. By doing that, we divide the input space to form the premise part of the fuzzy rules and the consequence part of each fuzzy rule is newly' organized based on center points of data group extracted by the C-Means clustering, It concerns the fuzzy model related parameters such as the number of input variables to be used in fuzzy model. a collection of specific subset of input variables, the number of membership functions according to used variables, and the polynomial type of the consequence part of fuzzy rules, The simultaneous optimization mechanism is explored. It can find optimal values related to structure and parameter of fuzzy model via PGA, the C-means clustering and standard least square method at once. A comparative analysis demonstrates that the Dnmosed algorithm is superior to the conventional methods.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.111-115
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• 2001

In this paper, a simple and systematic control design method is proposed for a discrete-time Takagi-Sugeno(TS) fuzzy system, which employs the parallel distributed compensation(PDC) to determine the structure of a fuzzy controller so as to mark all the Lyaunov exponents of the controlled TS fuzzy system strictly positive. This approach is proven to be mathematically rigorous for anticontrol of chaos for a TS fuzzy system in the sense that any given discrete-time TS fuzzy system can be made chaotic by the designed PDC controller along with the-operation. A numerical example is included to visualize the anticontrol effect.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.71-74
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• 1995

In this paper, a new PID fuzzy controller(FC) based on parallel operation of PI and PD fuzzy control is presented. First, two fuzzy rule bases are constructed by separating the linguistic control rule for PID FC into two parts : one is e-.DELTA.e part, and the other is .DELTAL.$^{2}$e-.DELTA.e part. And then two FCs employing these rule bases indivisually are synthesized and run in parallel. The incremental control input is determined by taking weighted mean of the outputs of two FCs. The proposed PID FC improves the transient response of the system and gives better performance than the conventional PI FC.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.1
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• pp.81-90
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• 2000

Parallel manipulator is suitable for the high precise task because it than has higher stiffness, larger load capacity and more excellent precision, due to the closed-lop structure, than serial manipulator. But the controller design for parallel manipulator is difficult because the parallel manipulator has both the complexity of structure and the interference of actuators. The precision improvement of parallel manipulator using a classical linear control scheme is difficult because the parallel manipulator has the tough nonlinear characteristics. In this paper, firstly, the kinematic analysis of a parallel manipulator used at the experiments is performed so as to show the controllability. The analysis of internal singular configuration of the workspace is performed using the kinematic isotropic index so a sto show the limitation of control performance of a simple linear controller with fixed control gains. Secondly, a control scheme is designed by using an adaptive fuzzy logic controller so that active joints of the parallel manipulator track more precisely the desired input trajectory. This adaptive fuzzy logic controller so that active joints of the parallel manipulator track more precisely the desired input trajectory. This adaptive fuzzy logic controller is often used for the control of nonlinear system because it has both the inference ability and the learning ability. Lastly, the effeciency of designed control scheme is demonstrated by the real-time control experiments with IBM PC interface logic H/W and S/W of my won making. The experimental results was a success.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.981-984
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• 1993

During the past decade, several specific hardwares for fast fuzzy inference have been developed. Most of them are dedicated to a specific inference method and thus cannot support other inference methods. In this paper, we present a hardware architecture called KAFA(KAist Fuzzy Accelerator) which provides various fuzzy inference methods and fuzzy set operators. The architecture has SIMD structure, which consists of two parts; system control/interface unit(Main Controller) and arithmetic units(FPEs). Using the parallel processing technology, the KAFA has the high performance for fuzzy information processing. The speed of the KAFA holds promise for the development of the new fuzzy application systems.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.45.1-45
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• 2001

This paper proposes a Mamdani fuzzy PID controller for controlling a process with small dead time. The controller composes of a parallel structure of fuzzy PI controller and fuzzy PD controller. Each controller has two inputs, error and change of error. Hence, the control signal of the proposed controller is the average value of the output of the fuzzy PI and PD controllers. The Mamdani fuzzy PID controller is easily to be adjusted to meet the desired control system performances both in transient state and steady state. The simulation results of the proposed Mamdani fuzzy PID controller by using the same parameters (proportional gain, integral time and derivative time) as the conventional PID controller are shown. The response of the Mamdani fuzzy PID control system is faster than the conventional PID control system. Both system responses have ...

• Structural Engineering and Mechanics
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• v.43 no.2
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• pp.163-177
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• 2012

This paper presents a LMI(linear matrix inequality)-based fuzzy approach of modeling and active vibration control of geometrically nonlinear flexible plates with piezoelectric materials as actuators and sensors. The large-amplitude vibration characteristics and dynamic partial differential equation of a piezoelectric flexible rectangular thin plate structure are obtained by using generalized Fourier series and numerical integral. Takagi-Sugeno (T-S) fuzzy model is employed to approximate the nonlinear structural system, which combines the fuzzy inference rule with the local linear state space model. A robust fuzzy dynamic output feedback control law based on the T-S fuzzy model is designed by the parallel distributed compensation (PDC) technique, and stability analysis and disturbance rejection problems are guaranteed by LMI method. The simulation result shows that the fuzzy dynamic output feedback controller based on a two-rule T-S fuzzy model performs well, and the vibration of plate structure with geometrical nonlinearity is suppressed, which is less complex in computation and can be practically implemented.

• 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.