• 한국지능시스템학회논문지
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• 제13권1호
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• pp.37-44
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• 2003

본 논문은 DNA coding 방법과 Genetic Algorithm(GA)을 사용하여 numeric(0~9) 패턴인식 성능을 비교 평가하였다. 이진스트링의 개체 집단 위에서 모의진화를 일으켜 효율적으로 최적 해를 탐색하는 GA와, 생체 분자인 DNA를 계산의 도구 및 정보 저장도구로 사용하며, Adenine(A), Cytosine(C), Guanine(G), Thymine(T)등의 4가지 염기를 사용하는 DNA coding 방법을 이용하여 numeric 패턴인식을 수행하였다. DNA coding 방법과 GA의 성능을 비교 평가하기 위해서 selection, crossover, mutation 등의 GA연산자를 DNA coding에 동일하게 적용하였다. 실험결과, DNA coding 방법은 GA보다 효과적으로 패턴인식을 수행하였다. GA에 비해 DNA coding 방법의 장점은 스트링의 길이가 가변적이고 해의 중복성을 가지며, 4가지 염기를 이용하기 때문에 해 표현이 다양함을 가지고 있다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 추계학술대회 논문집 학회본부 B
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• pp.524-526
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• 1999

In the construction of successful fuzzy models and/or controllers for nonlinear systems, the identification of a good fuzzy inference system is an important yet difficult problem, which is traditionally accomplished by a time-consuming trial-and-error process. In this paper, we propose a systematic identification procedure for complex multi-input single-output nonlinear systems with DNA coding method. A DNA coding method is optimization algorithm based on biological DNA as conventional genetic algorithms(GAs) are. The strings in the DNA coding method are variable-length strings, while standard GAs work with a fixed-length coding scheme. the DNA coding method is well suited to learning because it allows a flexible representation of a fuzzy inference system. We also propose a new coding method fur applying the DNA coding method to the identification of fuzzy models. This coding scheme can effectively represent the zero-order Takagi-Sugeno(TS) fuzzy model. To acquire optimal TS fuzzy model with higher accuracy and economical size, we use the DNA coding method to optimize the parameters and the number of fuzzy inference system. In order to demonstrate the superiority and efficiency of the proposed scheme, we finally show its application to a Duffing-forced oscillation system.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2002년도 추계학술대회 및 정기총회
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• pp.383-386
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• 2002

In this paper, we investigated the pattern recognition performance of the numeric patterns (from 0 to 9) using DNA coding method. The pattern recognition performance of the DNA coding method is compared to the that of the GA(Genetic Algorithm). GA searches effectively an optimal solution via the artificial evolution of individual group of binary string using binary coding, while DNA coding method uses four-type bases denoted by A(Adenine), C(Cytosine), G(Guanine) and T(Thymine), The pattern recognition performance of GA and DNA coding method is evaluated by using the same genetic operators(crossover and mutation) and the crossover probability and mutation probability are set the same value to the both methods. The DNA coding method has better characteristics over genetic algorithms (GA). The reasons for this outstanding performance is multiple possible solution presentation in one string and variable solution string length.

• 한국지능시스템학회논문지
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• 제13권6호
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• pp.737-742
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• 2003

본 논문에서는 DNA 코딩 방법을 이용하여 새로운 웨이블렛 기반 퍼지 모델링 방법을 제안한다. DNA 코딩 방법은 기존의 유전 알고리즘에 비해 지식 표현에 있어서 더 다양하고, 최적화 수행에 있어서 더 좋다고 알려져 있다 그 이유는 DNA 코딩 방법은 생물학적 DNA에 기반하여 더 풍부한 유전 정보를 암호화할 수 있기 때문이다. 제안한 방법은 웨이블렛 변환 기법을 사용함으로써 퍼지 모델을 생성한다. 여기서, 계수들은 DNA 코딩 방법을 이용하여 동정된다. 즉, 웨이블렛 변환과 DNA 코딩 방법의 장점들을 사용함으로써 더 좋은 퍼지 모델을 생성한다. 제안된 방법의 우수성을 증명하기 위해서 기존지 유전알고리즘과 그 결과를 비교한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제3권1호
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• pp.121-126
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• 2003

In this paper, we propose a new wavelet-based fuzzy modeling using a DNA coding method. Generally, it is well known that the DNA coding method is more diverse in the knowledge expression and better in the optimization performance than the genetic algorithm (GA) because it can encode more plentiful genetic informations based on the biological DNA. The proposed method can construct a fuzzy model using the wavelet transform, in which the coefficients are identified by the DNA coding method. Thus, we can effectively get the fuzzy model of the nonlinear system by using the advantages of both wavelet transform and DNA coding method. In order to demonstrate the superiority of the proposed method, it is compared with modeling method using the conventional GA.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2314-2316
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• 2000

In the construction of successful fuzzy models and/or controllers for nonlinear systems, identification of a good fuzzy Neural inference system is an important yet difficult problem, which is traditionally accomplished by trial and error process. In this paper, we propose a systematic identification procedure for complex multi-input single- output nonlinear systems with DNA coding method.DNA coding method is optimization algorithm based on biological DNA as are conventional genetic algothms (GAs). We also propose a new coding method for applying the DNA coding method to the identification of fuzzy Neural models. To acquire optimal TS fuzzy model with higher accuracy and economical size, we use the DNA coding method to optimize the parameters and the number of fuzzy inference system.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.225-228
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• 2001

DNA computing has been applied to the problem of getting an optimal solution since Adleman's experiment. DNA computing uses strings with various length and four-type bases that makes more useful for finding a global optimal solutions of the complex multi-modal problems. This paper presents DNA coding method for finding optimal solution of the multi-modal function and compares the efficiency of this method with the genetic algorithms (GA). GA searches effectively an optimal solution via the artificial evolution of individual group of binary string and DNA coding method uses a tool of calculation or Information store with DNA molecules and four-type bases denoted by the symbols of A(Ademine), C(Cytosine), G(Guanine) and T(Thymine). The same operators, selection, crossover, mutation, are applied to the both DNA coding algorithm and genetic algorithms. The results show that the DNA based algorithm performs better than GA.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 춘계학술대회 학술발표 논문집
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• pp.16-19
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• 1998

본 논문에서는 생물학적인 DNA와 인위적인 DNA의 개발 메커니즘에 근거를 둔 새로운 DNA coding mithod를 소개한다. 이 방법은 DNA 유전자의 Redundancy 성질과 Over-lapping 성질을 분석하여 구성한 알고리즘으로, DNA 자체의 특성 때문에 염색체의 길이를 자유자재로 변화시킬 수 있는 장점을 가지고 있다. 즉 삽입과 삭제의 과정을 쉽게 처리할 수 있다. 따라서 본 연구에서는 이 DNA coding method를 사용하여 시스템 제어에 필요한 지식을 표현하는 적당한 퍼지규칙을, 전건부와 후건부의 구조 및 매개변수 동조를 통하여, 획득하고, 이 규칙을 새로운 유전자 알고리즘을 이용하여 최적화 시킬 수 있는 새로운 퍼지규칙 획득 알고리즘을 개발한다. 이 방법의 유용성을 입증하기 위하여 물탱크 시스템에 적용한 결과 효과적으로 퍼지규칙을 획득한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.280-280
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• 2000

In this paper, we propose a method of constructing equation using bio-inspired emergent and evolutionary concepts. This method is algorithm that is based on the characteristics of the biological DNA and growth of plants. Here is. we propose a constructing method to make a DNA coding method for production rule of L-system. L-system is based on so-called the parallel rewriting mechanism. The DNA coding method has no limitation in expressing the production rule of L-system. Evolutionary algorithms motivated by Darwinian natural selection are population based searching methods and the high performance of which is highly dependent on the representation of solution space. In order to verify the effectiveness of our scheme, we apply it to one step ahead prediction of Mackey-Glass time series.

• 대한전기학회논문지:시스템및제어부문D
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• 제52권4호
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• pp.238-245
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• 2003

This paper presents a novel method for searching stable solution using DNA coding scheme. Often there are more than one solutions that satisfy the system requirements. These solutions can be viewed as extremes in multimodal function. All extremes are not the same in that some of them are using sensitive to noise or perturbation. This paper address the method that selects a solution that meets the system requirements in terms of output performance and is tolerant to the perspective noise or perturbation. A new method, called a Gradient DNA coding is proposed to achieve such objectives several numerical examples and presented and comparing DNA coding with genetic algorithm is also given.