• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.255-258
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• 2001

This paper is implementation of cellular automata neural network system which is a living creatures' brain using evolving hardware concept. Cellular automata neural network system is based on the development and the evolution, in other words, it is modeled on the ontogeny and phylogeny of natural living things. The proposed system developes each cell's state in neural network by CA. And it regards code of CA rule as individual of genetic algorithm, and evolved by genetic algorithm. In this paper we implement this system using evolving hardware concept Evolving hardware is reconfigurable hardware whose configuration is under the control of an evolutionary algorithm. We design genetic algorithm process for evolutionary algorithm and cells in cellular automata neural network for the construction of reconfigurable system. The effectiveness of the proposed system is verified by applying it to time-series prediction.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제9권2호
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• pp.83-89
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• 2005

In this paper a new algorithm of learning and evolving artificial neural networks using gene expression programming (GEP) is presented. Compared with other traditional algorithms, this new algorithm has more advantages in self-learning and self-organizing, and can find optimal solutions of artificial neural networks more efficiently and elegantly. Simulation experiments show that the algorithm of evolving weights or thresholds can easily find the perfect architecture of artificial neural networks, and obviously improves previous traditional evolving methods of artificial neural networks because the GEP algorithm imitates the evolution of the natural neural system of biology according to genotype schemes of biology to crossover and mutate the genes or chromosomes to generate the next generation, and the optimal architecture of artificial neural networks with evolved weights or thresholds is finally achieved.

• Journal of Information Processing Systems
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• 제5권1호
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• pp.1-4
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• 2009

Level set methods are the numerical techniques for tracking interfaces and shapes. They have been successfully used in image segmentation. A new variational level set evolving algorithm without re-initialization is presented in this paper. It consists of an internal energy term that penalizes deviations of the level set function from a signed distance function, and an external energy term that drives the motion of the zero level set toward the desired image feature. This algorithm can be easily implemented using a simple finite difference scheme. Meanwhile, not only can the initial contour can be shown anywhere in the image, but the interior contours can also be automatically detected.

• Journal of Advanced Marine Engineering and Technology
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• 제25권2호
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• pp.383-394
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• 2001

The stabilization control of inverted pendulum (IP) system is difficult because of its nonlinearity and structural unstability. In this paper, an Evolving Neural Network Controller (ENNC) without Error Back Propagation (EBP) is presented. An ENNC is described simply by genetic representation using an encoding strategy for types and slope values of each active functions, biases, weights and so on. By an evolutionary programming which has three genetic operation; selection, crossover and mutation, the predetermine controller is optimally evolved by updating simultaneously the connection patterns and weights of the neural networks. The performances of the proposed ENNC(PENNC)are compared with the one of conventional optimal controller and the conventional evolving neural network controller (CENNC) through the simulation and experimental results. And we showed that the finally optimized PENNC was very useful in the stabilization control of an IP system.

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2001년도 춘계학술대회 논문집
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• pp.243-248
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• 2001

The stabilization control of Inverted Pendulum(IP) system is difficult because of its nonlinearity and structural unstability. Thus, in this paper, an Evolving Neural Network Controller(ENNC) without Error Back Propagation(EBP) is presented. An ENNC is described simply by genetic representation using an encoding strategy for types and slope values of each active functions, biases, weights and so on. By an evolutionary programming which has three genetic operation; selection, crossover and mutation, the predetermine controller is optimally evolved by updating simultaneously the connection patterns and weights of the neural networks. The performances of the proposed ENNC(PENNC) are compared with the ones of conventional optimal controller and the conventional evolving neural network controller(CENNC) through the simulation and experimental results. And we showed that the finally optimized PENNC was very useful in the stabilization control of an IP system.

• 한국지능시스템학회논문지
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• 제11권5호
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• pp.426-432
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• 2001

본 논문은 진화형 하드웨어를 이용하여 생물의 정보처리 시스템인 셀룰라 오토마타 신경망의 구현에 관한 연구이다. 셀룰라 오토마타 신경망은 진화 및 발생을 기반으로 한 신경망 모델이다. 진화는 다양성을 주요 근원을 제공하는 돌연변이 및 재 조합 비율에 의하여 비결정론이며, 발생은 결정론 적이며 지역적인 무리현상을 따른다. 셀룰라 오토마타 신경망은 셀룰라 오토마타에 의해 신경망 내부의 각 셀의 상태를 발생시키고, 초기 셀을 유전자 알고리즘의 개체로 간주하여 초기 셀이 진화 알고리즘을 통해 진화함으로써 신경망이 진화하는 시스템이다. 본 논문은 이 시스템을 진화형 하드웨어 이용하여 하드웨어로 구현하였다. 진화형 하드웨어는 진화 알고리즘과 재구성하드웨어의 결합체이다. 즉, 재구성 하드웨어의 구성에 필요한 bit를 유전자 알고리즘의 개체로 간주한 것이다. 진화 알고리즘을 수행하기 위해 유전자 알고리즘 프로세서를 설계하였으며, 셀룰라 오토마타 신경망이 유전자 알고리즘의 개체와 셀룰라 오토마타 룰에 의해 자동적으로 신경망을 생성하기 위해 신경망을 이루는 셀들로 설계하였다. 제안된 시스템의 효율성을 검증하기 위해 Exclusive-OR 문제에 적용하였다.

• 한국지능시스템학회논문지
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• 제15권2호
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• pp.204-210
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• 2005

본 논문에서는 TSK 퍼지 모델을 위한 온라인 식별 알고리즘을 제안한다. 제안된 알고리즘은 거리를 이용하여 TSK 퍼지 모델에 대한 전건부의 구조를 식별하고, 재귀적 최소자승법으로 후건부를 구성하는 부분 선형 함수들의 매개 변수를 구한다. 대부분의 다른 연구들에서는 전건부의 구조를 구하기 위해서 클러스터링을 수행할 때 입력 공간에서만 고려하였으나. 제안된 알고리즘에서는 입력 공간 및 출력 공간 모두에서 고려하여, 아웃라이어를 효과적으로 배제할 수 있다. 기존의 대부분의 다른 알고리즘에서 샘플 데이터자체를 클러스터의 중심으로 사용하여 잡음에 민감한 단점이 있었으나, 제안된 알고리즘에서는 데이터 자체를 클러스터의 중심으로 사용하지 않아 잡음에 대해 민감하지 않다. 제안된 알고리즘은 많은 데이터의 저장을 필요로 하지 않고, 한 번 통과함으로써 모델을 구할 수 있다.

• 한국지능시스템학회논문지
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• 제11권3호
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• pp.231-240
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• 2001

Conventional expert systems has been criticized due to its lack of capability to adapt to the changing decision-making environments. In literature, many methods have been proposed to make expert systems more environment-adaptive by incorporating fuzzy logic and neural networks. The objective of this paper is to propose a new approach to building a self-evolving expert system inference mechanism by integrating fuzzy neural network and fuzzy rule extraction technique. The main recipe of our proposed approach is to fuzzify the training data, train them by a fuzzy neural network, extract a set of fuzzy rules from the trained network, organize a knowledge base, and refine the fuzzy rules by applying a pruning algorithm when the decision-making environments are detected to be changed significantly. To prove the validity, we tested our proposed self-evolving expert systems inference mechanism by using the bankruptcy data, and compared its results with the conventional neural network. Non-parametric statistical analysis of the experimental results showed that our proposed approach is valid significantly.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2003년도 Proceeding
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• pp.99-104
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• 2003

In this research, we proposed the mechanism to develop self evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most former researchers tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, thy have some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, many of researchers had tried to develop an automatic knowledge extraction and refining mechanisms. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, in this study, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference. Our proposed mechanism has five advantages empirically. First, it could extract and reduce the specific domain knowledge from incomplete database by using data mining algorithm. Second, our proposed mechanism could manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it could construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems). Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic. Fifth, RDB-driven forward and backward inference is faster than the traditional text-oriented inference.

• 전기학회논문지
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• 제57권9호
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• pp.1628-1635
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• 2008

In this paper, the motion coordination algorithm of mobile agents in active sensor network is proposed to track the dynamic boundary for environmental monitoring. While most of dynamic boundary tracking algorithms in the literature were studied under the assumption that the boundary and/or its evolving rate is known a priori, the proposed algorithm is assumed that the individual active agent can measure the state of environment locally without any information of the boundary. When the boundary is evolving dynamically, the formation of active agents is designed to achieve two objectives. One is to track boundary layer based on the measured information and a small deviation. The other is to maintain a uniform distance between adjacent agents. The algorithm structure based on a state diagram is proposed to achieve these two objectives. Finally, it will be shown in the simulations that all given agents converge to a desired boundary layer and maintain a formation along the boundary. (e.g., a circle, an ellipse, a triangle and a rectangle)