• 한국지능시스템학회논문지
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• 제2권2호
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• pp.34-41
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• 1992

A robust and reliable learning and reasoning mechanism is addressed based upon fuzzy set theory and fuzzy associative memories. The mechanism stores a priori an initial knowledge base via approximate learning and utilizes this information for decision-making systems via fuzzy inferencing. We called this fuzzy computer architecture a 'fuzzy hypercube' processing all the rules in one clock period in parallel. Fuzzy hypercubes can be applied to control of a class of complex and highly nonlinear systems which suffer from vagueness uncertainty. Moreover, evidential aspects of a fuzzy hypercube are treated to assess the degree of certainty or reliability together with parameter sensitivity.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.412-416
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• 1993

In this paper, fuzzy-neural network is proposed to identify the Activated Sludge Process(A.S.P) in sewage treatment such as "IF-THEN" type fuzzy rules and using various learning methods and improved complex method, the performance index of the identified model is improved. The proposed FNN has the neural network structure of which the connection weights have particular meanings for obtaining fuzzy inference rules and for tuning membership functions. And based on the identified model, graphic simulator which can analize nonlinear characteristics of A.S.P and generate control strategy for A.S.P is being developed.developed.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.484-489
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• 1992

The inference of fuzzy controller can be considered a mapping from the controller input to membership value. The membership value, a kind of weight, has a role to decide if the input is appropriate to the rule. The membership function is described by several values, which are decided by a learning method. The learning method is adopted from adaptive filtering theory. The simulation shows the proposed fuzzy controller can learn linear and nonlinear functions. the structure of the proposed fuzzy controller becomes a kind of neural network.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2003년도 춘계 학술발표회 논문집
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• pp.183-189
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• 2003

A momentum threshold autoregressive (MTAR) model, a nonlinear autoregressive model, is analyzed in a Bayesian framework. Parameter estimation in the presence of missing data is done by using Markov chain Monte Carlo methods. We also propose simple Bayesian test procedures for asymmetry and unit roots. The proposed method is applied to a set of Korea unemployment rate data and reveals evidence for asymmetry and a unit root.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1181-1186
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• 1993

In the paper, a new design method of rule-based fuzzy modeling is proposed for model identification of nonlinear systems. The structure indentification is carried out, utilizing fuzzy c-means clustering. Fuzzy-neural networks composed back-propagation algorithm and linear fuzzy inference method, are used to identify parameters of the premise and consequence parts. To obtain optimal linguistic fuzzy implication rules, the learning rates and momentum coefficients are tuned automatically using a modified complex method.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 추계학술대회 학술발표 논문집 제16권 제2호
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• pp.323-326
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• 2006

Rotational inverted pendulum is a typical under-actuated system. For its highly nonlinear characteristic, a sliding mode controller is chosen for its robustness against the system uncertainties. Tow fuzzy inference mechanisms are applied in this paper to reduce the chattering phenomenon. One is proposed to construct a time-varying sliding surface. Another one is used to obtain the minimum upper bound of the uncertainties. A comparison between the conventional sliding mode and the fuzzy sliding mode is shown by simulations.

• International Journal of Control, Automation, and Systems
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• 제4권6호
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• pp.756-762
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• 2006

Shape Memory Alloy(SMA) actuators, which have the ability to return to a predetermined shape when heated, have many potential applications such as aeronautics, surgical tools, robotics and so on. Although the conventional PID controller can be used with slow response systems, there has been limited success in precise motion control of SMA actuators, since the systems are disturbed by unknown factors beside their inherent nonlinear hysteresis and changes in the surrounding environment of the systems. This paper presents a new development of a SMA position control system by using a self-tuning fuzzy PID controller. This control algorithm is used by tuning the parameters of the PID controller thereby integrating fuzzy inference and producing a fuzzy adaptive PID controller, which can then be used to improve the control performance of nonlinear systems. The experimental results of position control of SMA actuators using conventional and self-tuning fuzzy PID controllers are both included in this paper.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.507-509
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• 1999

In this paper, we propose a new fuzzy model identification method that can yield a successful fuzzy rule base for fundamental approximations. The method in this paper uses a set of input-output data and is based on a hybrid messy genetic algorithm (mGA) with a fine-tuning scheme. The mGA processes variable-length strings, while standard GAs work with a fixed-length coding scheme. For successfully identifying a complex nonlinear system, we first use the mGA, which coarsely optimizes the structure and the parameters of the fuzzy inference system, and then the gradient descent method which tine tunes the identified fuzzy model. In order to demonstrate the superiority and efficiency of the proposed scheme, we finally show its application to a nonlinear approximation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1992년도 하계학술대회 논문집 A
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• pp.404-407
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• 1992

In this thesis, the algorithm of Fuzzy Logic Control(FLC) is applied to the Nonlinear system to implement a system response. Proportional-Integral-Derivative(PID) controller is also used to control the various systems. Look-up table is applied to decide the control input, and the other look-up table is added for saving memories and inference time. Generally, FLC input variables are error(E) and error derivative($\Delta$E). In this algorithm. another Input variable error's second derivative($\Delta^2$E) is added for Robust Fine control.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1999년도 제14차 학술회의논문집
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• pp.169-172
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• 1999

In this paper, piecewise quadratic Lyapunov functions are used to analyze the stability of fuzzy-model-based controller. We represent the nonlinear system using a Takagi-Sugeno fuzzy model, which represent the given nonlinear system by fuzzy inference rules and local linear dynamic models. The proposed stability analysis technique is developed by dividing the whole fuzzy system into the smaller separate fuzry systems to reduce the conservatism. Some necessary and sufficient conditions for the proposed method are obtained. Finally, stability of the closed system with various kinds of controller for TS fuzzy model is checked through the proposed method.