• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.417-422
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• 2007

A new evolution method termed queen-bee and mutant-bee evolution is based on the previous queen-bee evolution [1]. Even though the queen-bee evolution has shown very good performances, two parameters for strong mutation are added to the genetic algorithms. This makes the application of genetic algorithms with queen-bee evolution difficult because the values of the two parameters are empirically decided by a trial-and-error method without a systematic method. The queen- bee and mutant-bee evolution has no this problem because it does not need additional parameters for strong mutation. Experimental results with typical problems showed that the queen-bee and mutant-bee evolution produced nearly similar results to the best ones of queen-bee evolution even though it didn't need to select proper values of additional parameters.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.4
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• pp.254-268
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• 2013

For real-world clustering tasks, the input data is typically not easily separable due to the highly complex data structure or when clusters vary in size, density and shape. Kernel-based clustering has proven to be an effective approach to partition such data. In this paper, we provide an overview of several fuzzy kernel clustering algorithms. We focus on methods that optimize an fuzzy C-mean-type objective function. We highlight the advantages and disadvantages of each method. In addition to the completely unsupervised algorithms, we also provide an overview of some semi-supervised fuzzy kernel clustering algorithms. These algorithms use partial supervision information to guide the optimization process and avoid local minima. We also provide an overview of the different approaches that have been used to extend kernel clustering to handle very large data sets.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.9
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• pp.95-103
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• 1995

Recently the trends toward reduction in size of industrial products have increased the application of micro drilling. But micro drilling has still much difficulty so that the needs for active control which give adaptation to controller are expanding. In this paper initial cutting condition was determined for some sorkpieces by experiment and GA-based Fuzzy controller was devised by genetic algorithms and fuzzy inference. The fuzzy inference has been applied to the various prob- lems. However the determination of the membership function is one of the difficult problem. So we introduce a genetic algorithms and propose a self-tuning method of fuzzy membership function. Based on this intelligent control, automation of micro drilling was carried out like the cutting process of skilled machinist.

• Industrial Engineering and Management Systems
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• v.4 no.1
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• pp.109-116
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• 2005

Proposed in this paper is an intelligent management system for evaluating science research projects based on fuzzy neural networks with genetic algorithms. This system was planned, designed and tested employing theories and approaches of software engineering. This system was then applied to evaluate science research projects of the Natural Science Foundation of Guangdong Province, People’s Republic of China. The outcome / results shows the feasibility and validity of the system and its possible application to other intelligent management systems.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.575-580
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• 2003

In this paper, we develop a control hardware such as an FPGA based general purpose controller with a DSP board to solve nonlinear control problems. PID control algorithms are implemented in an FPGA and neural network control algorithms are implemented in a DSP board. PID controllers implemented on an FPGA was designed by using VHDL to achieve high performance and flexibility. By using high capacity of an FPGA, the additional hardware such as an encoder counter and a PWM generator, can be implemented in a single FPGA device. As a result, the noise and power dissipation problems can be minimized and the cost effectiveness can be achieved. In order to show the performance of the developed controller, it was tested for controlling nonlinear systems such as an inverted pendulum.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.3 no.1
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• pp.115-125
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• 1999

Simple Genetic Algorithm(SGA) proposed by J. H. Holland is a population-based optimization method based on the principle of the Darwinian natural selection. The theoretical foundations of GA are the Schema Theorem and the Building Block Hypothesis. Although GA does well in many applications as an optimization method, still it does not guarantee the convergence to a global optimum in some problems. In designing intelligent systems, specially, since there is no deterministic solution, a heuristic trial-and error procedure is usually used to determine the systems' parameters. As an alternative scheme, therefore, there is a growing interest in a co-evolutionary system, where two populations constantly interact and co-evolve. In this paper we review the existing co-evolutionary algorithms and propose co-evolutionary schemes designing intelligent systems according to the relation between the system's components.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.117-120
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• 2001

This paper presents a new global state-matching intelligent digital redesign method for nonlinear systems by using genetic algorithms (GAs). The proposed method results in global matching of the states of the analogously controlled system with those of the digitally controlled system while the conventional intelligent digital redesign method does not. The proposed method provides a new approach for the digital redesign of a class of fuzzy-model-based controllers.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1792-1795
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• 1997

It is difficulte to control in the flow system because there are many disturbance. So it is impossible to control delicately sometimes by PI or PID. In this paper, we study on the application of intellignet control algorithms such as 2DOF PID control, neural network, Fuzzy contro, Relay feedback to the flow control system. the resultings are 2DOF-PID control is more good response.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.237-240
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• 2007

본 논문에서는 유전자 발현데이터로부터 유전자 조절네트워크를 추론하는 유전자 알고리즘을 제안한다. 근래에 유전자 알고리즘을 이용하여 유전자 조절네트워크를 추론하려는 시도가 있었으나 그리 성공적이지 못하였다. 우리는 본 논문에서 유전자 조절네트워크를 보다 효율적으로 추론할 수 있게 하기 위하여 새로운 유전자 인코딩 기법을 개발하여 적용하였다. 선형 유전자 조절네트워크로 모델링 된 인공 유전자 조절네트워크를 사용하여 실험한 결과 대부분의 경우에 있어서 주어진 인공 유전자 조절네트워크와 유사한 네트워크를 추론하였으며 완전히 동일한 유전자네트워크를 추론하기도 하였다. 향후 실제 유전자 발현 데이터를 이용하여 추론해 보는 것이 필요하다.

• The Journal of Korea Robotics Society
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• v.2 no.1
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• pp.9-20
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• 2007

This paper presents a theoretical study for making intelligent robots with human-like mind. For the development of a cognitive mental model, we developed three algorithms based on the cognitive process for human psychoanalysis. Specifically, the concept of id, ego and superego from the theory of Sigmund Freud was adopted and the procedural algorithms were presented.