• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.707-712
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• 1998

This paper presents several softcomputing techniques such as neural networks, fuzzy logic and genetic algorithms : Neural networks as brain metaphor provide fundamental structure, fuzzy logic gives a possibility to utilize top-down knowledge from designer, and genetic algorithms as evolution metaphor determine several system parameters with the process of bottom up development. With these techniques, we develop a pattern recognizer which consists of multiple neural networks aggregated by fuzzy integral in which genetic algorithms determine the fuzzy density values. The experimental results with the problem of recognizing totally unconstrained handwritten numeral show that the performance of the proposed method is superior to that of conventional methods.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.42.1-42
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• 2002

A model to predict population by sex, age and district over a long-range period is proposed based on fuzzy theories. First, a fuzzy model is described. Second, a method to estimate the social increase by sex and age in each district is proposed based on a fuzzy clustering method for dealing with long-range socioeconomic changes in population migration. By the proposed methods, it became possible to predict the population by sex, age and district over a long-range period. Third, the structure and characteristics of the three models of employment model, time distance model, and land use model constructed to predict various socioeconomic indicators, which are require...

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.418-420
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• 1989

Many complex industrial processes cannot be satisfactorily controlled using the results of modern control theory, mainly because their precise structure is unknown. However this is often balanced by a considerable amount of operator's heuristic knowledges for the process which is difficult to quantify and utilize. Fuzzy set theory is a relatively new concept which allows this qualitativeness to be expressed rigorously and therefore in this paper modified PI type fuzzy logic controller is introduced and its usefulness for control is assessed.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.742-748
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• 1994

Scientific involvement in complex decision-making systems, characterized by multicriteria phenomena and fuzziness inherent in the structure of information, requires suitable methods. Especially, when powerful dependent criteria are introduced, the systems are become more complex. This paper presents a fuzzy dependence relation model for this kind of multicriteria decision-making. The model we propose is based on fuzzy relation in fuzzy system theory. For the application of the model a numerical example is quoted.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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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.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.348-348
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• 2000

In this paper, we present a fuzzy moving sliding mode control for two-degrree-of-freedom robotic manipulator. 17he sliding surface parameters are designed by fuzzy inference. The proposed sliding mode control makes the error always remain on the surface from beginning and therefore, the system is insensitive to system uncertaintics and external disturbances. Simulation results show the effectiveness of proposed scheme.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.512-514
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• 1998

The paper presents a model reference adaptive control containing a fuzzy algorithm for tuning the gain coefficient which adjusts the level of the fuzzy controller output. The synthesis of a fuzzy tuning algorithm has been performed for the inverted pendulum system. The computer simulation results have proved the efficiency of the proposed method, showing stable system responses.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.403-405
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• 1997

In this paper, a navigation system based on fuzzy logic controllers is developed for a mobile robot in an unknown environment. The structure of this fuzzy navigation system features sensor system, fuzzy controllers for motion planning and the motion control system for real-time execution.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1121-1124
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• 1993

We propose two-degree-of-freedom fuzzy neural network control systems. It has a hierarchical structure of two sets of control knowledge, thus it is easy to extract and refine fuzzy rules before and after the operation has started, and the number of fuzzy rules is reduced. In addition an example application of automatic vehicle operation is reported and its usefulness is shown simulation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1114-1117
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• 1993

The paper presents an effective method for finding the propagation structure of the real origin of a system malfunction. It uses a combined system model consisting of Structural Model (SM) in the form of Fuzzy Directed Graph and Behavior Model (BM) as a set of Fuzzy Relational Equations $A\;{\circ}\;R\;=\;B$. Here a specially proposed fuzzy inference technique is checked and investigated. Finally a test example for fault diagnosis of an industrial system is given and analyzed.