• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.87-91
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• 2002

The problem of maneuvering target tracking has been studied in the field of the state estimation over decades. The Kalman filter has been widely used to estimate the state of the target, but in the presence of a maneuver, its performance may be seriously degraded. In this paper, to solve this problem and track a maneuvering target effectively, a DNA coding-based interacting multiple model (DNA coding-based IMM) method is proposed. The proposed method can overcome the mathematical limits of conventional methods by using the fuzzy logic based on DNA coding method. The tracking performance of the proposed method is compared with those of the adaptive IMM algorithm and the GA-based IMM method in computer simulations.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2176-2178
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• 2003

This paper presents the intelligent modeling method of chaotic systems using a DNA coding based wavelet neural network(WNN). Generally the mathematical teaming method such as gradient descent method is used to adjust the parameters of WNN, which has much computational cost. To overcome this kind of problem, we use the DNA coding method as the learning method of WNN, and then combine it with the WNN. Finally, to verify the efficiency of our method, we apply the proposed DNA coding based wavelet neural network for modeling of Duffing system, which is a representative continuous-time chaotic system.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.370-372
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• 2004

This paper addresses the wavelet-based fuzzy PI/PD controller design using DNA coding method. A structure of fuzzy controller model is adopted as the wavelet transform of which the coefficients are identified. The proposed method overcomes some mathematical limits of conventional methods by using the fuzzy logic that is optimized by DNA coding method. The feasibility of the proposed fuzzy controller design scheme is verified by applying to the servo control of the optical disk drive.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.107-110
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• 2000

In this paper, we propose a method of constructing neural networks 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.497-502
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• 2002

The problem of maneuvering target tracking has been studied in the field of the state estimation over decades. The Kalman filter has been widely used to estimate the state of the target, but in the presence of a maneuver, its performance may be seriously degraded. In this paper, to solve this problem and track a maneuvering target effectively, a DNA coding-based interacting multiple model (DNA coding-based W) method is proposed. The proposed method can overcome the mathematical limits of conventional methods by using the fuzzy logic based on DNA coding method. The tracking performance of the proposed method is compared with those of the adaptive IMM algorithm and the GA-based IMM method in computer simulations.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.4
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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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.118-121
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• 2002

The problem of maneuvering target tracking has been studied in the field of the state estimation over decades. The Kalman filter has been widely used to estimate the state of the target, but in the presence of a maneuver, its performance may be seliously degraded. In this paper, to solve this problem and track a maneuvering target effectively, DNA coding-based intelligent Kalman filter (DNA coding-based IKF) is proposed. The proposed method can overcome the mathematical limits of conventional methods and can effectively track a maneuvering target with only one filter by using the fuzzy logic based on DNA coding method. The tracking performance of the proposed method is compared with those of the adaptive interacting multiple model (AIMM) method and the GA-based IKF in computer simulations.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.2
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• pp.131-136
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• 2003

The problem of maneuvering target tracking has been studied in the field of the state estimation over decades. The Kalman filter has been widely used to estimate the states of the target, but in the presence of a maneuver, its performance may be seriously degraded. In this paper, to solve this problem and track a maneuvering target effectively, DNA coding-based intelligent Kalman filter (DNA coding-based IKF) is proposed. The proposed method can overcome the mathematical limits of conventional methods and can effectively track a maneuvering target with only one filter by using the fuzzy logic based on DNA coding method. The tracking performance of the proposed method is compared with those of the adaptive interacting multiple model (AIMM) method and the GA-based IKF in computer simulations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.322-325
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• 2003

In this paper, we propose n new fuzzy model identification of time series system using wavelet transform and genetic DNA code. 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 information 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.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.464-467
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• 1999

Rapid progress in the modeling of biological structures and simulation of their development has occurred over the last few years. Cellular automata (CA) and Lindenmayer-system(L-system) are the representative models of development/morphogenesis of multicellular organism. L-system is applied to the visualization of biological plant. Also, CA are applied to the study of artificial life and to the construction of an artificial brain. To design the L-system and CA automatically, we make this model evolve. It is necessary to code the developmental rules for evolution. In this paper, we propose a DNA coding method for evolution the models of development/morphogenesis of biological multicellular organisms. DNA coding has the redundancy and overlapping of gene and is apt for the representation of the rule. In this paper, we propose the DNA coding method of CA and L-system.