• 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.

• ETRI Journal
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• 제35권2호
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• pp.207-217
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• 2013

This paper introduces the modular cellular neural network (CNN), which is a new CNN structure constructed from nine one-layer modules with intercellular interactions between different modules. The new network is suitable for implementing many image processing operations. Inputting an image into the modules results in nine outputs. The topographic characteristic of the cell interactions allows the outputs to introduce new properties for image processing tasks. The stability of the system is proven and the performance is evaluated in several image processing applications. Experiment results on texture segmentation show the power of the proposed structure. The performance of the structure in a real edge detection application using the Berkeley dataset BSDS300 is also evaluated.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.253-253
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• 2000

Each robot plays a role of its own behavior in dynamic robot-soccer environment. One of the most necessary conditions to win a game is control of robot movement. In this paper we suggest a win strategy using Cellular Neural Network to set optimal path and cooperative behavior, which divides a soccer ground into grid-cell based ground and has robots move a next grid-cell along the optimal path to approach the moving target.

• 한국산학기술학회논문지
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• 제10권3호
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• pp.542-546
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• 2009

본 논문은 신경망을 이용한 간섭 신호 제어로써 합성 다층 퍼셉트론에 입각하여 셀룰라 이동통신에서의 수신된 신호들을 역전파 학습알고리즘을 이용하여 검파하는 것에 대하여 소개하였다. 그리고 컴퓨터 시뮬레이션 결과를 통하여 co-channel간섭과 협대역 간섭의 실제 음색에서 기존에 쓰여진 Rake수신기보다 더 낮은 비트 오차 확률을 가지는 NNAC(neural network adaptive correlator)에 대하여 분석 고찰하였다.

• 대한산업공학회지
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• 제24권1호
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• pp.23-35
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• 1998

This article presents a network flow analysis to form flexible machine cells with minimum intercellular part moves and a neural network model to form part families. The operational sequences and production quantity of the part, and the number of cells and the cell size are taken into considerations for a 0-1 quadratic programming formulation and a network flow based solution procedure is developed. After designing the machine cells, a neural network approach for the integration of part families and the automatic assignment of new parts to the existing cells is proposed. A multi-layer backpropagation network with one hidden layer is used. Experimental results with varying number of neurons in hidden layer to evaluate the role of hidden neurons in the network learning performance are also presented. The comprehensive methodology developed in this article is appropriate for solving large-scale industrial applications without building the knowledge-based expert rule for the cellular manufacturing environment.

• 한국정보통신학회논문지
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• 제6권6호
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• pp.819-826
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• 2002

셀룰러 뉴럴 네트워크는 국부적 연결특성을 가지고 있어 실시간 이미지처리에 적합한 뉴럴 네크워크이다. 또한 국부적 연결특징은 VLSI구현에 적합하다. 그의 응용분야는 패턴인식과 숫자인식 및 영상처리에 응용되고 있다. 본 논문에서, CNN은 전처리 단계로서 숫자의 특징점 추출에 이용된다. CNN을 이용한 그림자검출은 4내지 6방향으로 검출하여 숫자의 특징점을 방향별로 추출한다. 분류단계에서 이러한 형상자료는 다층BP뉴럴 네트워크의 입력벡터에 적합하도록 압축되어 입력된다. 실험결과 CNN을 통한 숫자인식은 굴림체의 경우96%이상의 인식율을 보여 만족할 만한 결과를 얻었다.

• 전자공학회논문지C
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• 제35C권5호
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• pp.59-68
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• 1998

This paper proposes a neural network algorithm for a channel assignment in cellular mobile communications. The proposed algorithm is developed base on hopfield neural network in order to minimize the number of channel without a confliction between cells. To compare the performance of the proposed algorithm, we used seven benchmark problems selected from kunz's and funabiki's papers. Experimental results show that the convergence times are reduced form 27% to 66% compared with Kunz's and funabiki's algorithm and vonvergence rates are improved to 100%.

• 한국산학기술학회논문지
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• 제14권3호
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• pp.1372-1377
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• 2013

본 논문은 신경망을 이용한 간섭 신호 제어로써 합성 다층 퍼셉트론에 입각하여 셀룰라 이동 통신에서의 수신된 신호들을 역전파 학습알고리즘을 이용하여 검파하는 것에 대하여 소개하였다. 그리고 컴퓨터 시뮬레이션 결과를 통하여 공동 간섭과 협대역 간섭의 실제 음색에서 기존에 쓰여진 레이크 수신기보다 더 낮은 비트 오차 확률을 가지는 NNAC(neural network adaptive correlator)에 대하여 분석 하였다.

• Korean Journal of Mathematics
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• 제30권3호
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• pp.491-502
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• 2022

In this paper we investigate some sufficient conditions to guarantee the existence and uniqueness of Stepanov-like weighted pseudo almost periodic solutions of cellular neural networks on Clifford algebra for non-automomous cellular neural networks with multi-proportional delays. Our analysis is based on the differential inequality techniques and the Banach contraction mapping principle.

• 한국통신학회논문지
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• 제21권4호
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• pp.1008-1018
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• 1996

For assigning channel in a cellular sireless network, we purpose satisfying constraints with a related probability between cells and channels on the channel interference matrix formed by m terminals and n channels. And we purpose to get efficient channel assigning to limited channels in a wireless cell using parallelism of neural networks. In this paper, we solve the problem according to the number of requeirements when channel change with 11-533 procession elements. We demonstrate efficiency of proposed algorithm through same simulations in a specific time period.