• 한국통신학회논문지
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• 제26권8B호
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• pp.1078-1084
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• 2001

In this paper, fuzzy-ARTMAP neural network is applied for compensating the nonlinearity of satellite communication channel. The fuzzy-ARTMAP is made of using fuzzy logic and ART neural network. By a match tracking process with vigilance parameter, fuzzy ARTMAP neural network achieves a minimax learning rule that minimizes predictive error and maximizes generalization. Thus, the system automatically learns a minimal number of recognition categories, or hidden units, to meet accuracy criteria. Simulation studies are performed over satellite nonlinear channels. The performance of proposed fuzzy-ARTMAP equalizer is compared with MLP-basis equalizers.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제2권1호
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• pp.43-48
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• 2002

This paper discusses the application of fuzzy-ARTMAP neural network to compensate the nonlinearity of satellite communication channel. The fuzzy-ARTMAP is the class of ART(adaptive resonance theory) architectures designed fur supervised loaming. It has capabilities not fecund in other neural network approaches, that includes a small number of parameters, no requirements fur the choice of initial weights, automatic increase of hidden units, and capability of adding new data without retraining previously trained data. By a match tracking process with vigilance parameter, fuzzy-ARTMAP neural network achieves a minimax teaming rule that minimizes predictive error and maximizes generalization. Thus, the system automatically leans a minimal number of recognition categories, or hidden units, to meet accuracy criteria. As a input-converting process for implementing fuzzy-ARTMAP equalizer, the sigmoid function is chosen to convert actual channel output to the proper input values of fuzzy-ARTMAP. Simulation studies are performed over satellite nonlinear channels. QPSK signals with Gaussian noise are generated at random from Volterra model. The performance of proposed fuzzy-ARTMAP equalizer is compared with MLP equalizer.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2001년도 춘계종합학술대회
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• pp.625-628
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• 2001

본 논문은 차량 번호판 영상을 안정적으로 추출하여 인식하는 방법으로 Fuzzy-ARTMAP 신경회로망을 이용하여 인식하는 시스템을 제안한다. 입력 영상에서 칼라정보를 이용하여 휘도값을 추출하고, 추출된 영상에서 히스토그램을 이용하여 번호판을 배경영상에서 분리하는 작업을 수행한 후, X축 영역에 축적 히스토그램을 적용하여 글자를 분리하고, Y축 영역에 축적 히스토그램을 이용하여 글자를 완전 분리하여 번호판의 문자를 분리시킨 후, 추출된 문자 영역을 Fuzzy-ARTMAP 신경망에 입력하여 문자를 인식하였다. Fuzzy-ARTMAP을 이용한 결과 기존의 다른 신경망을 이용한 것보다 문자인식 처리 시간을 단축시키고 인식률을 향상시킬 수 있었다.

• 전자공학회논문지B
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• 제33B권9호
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• pp.117-123
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• 1996

In this paper, we present a new learning method for the fuzzy ARTMAP which is effective for the noisy input patterns. Conventional fuzzy ARTMAP employs only fuzzy AND operation between input vector and weight vector in learning both top-down and bottom-up weight vectors. This fuzzy AND operation causes excessive update of the weight vector in the noisy input environment. As a result, the number of spurious categories are increased and the recognition ratio is reduced. To solve these problems, we propose a new method in updating the weight vectors: the top-down weight vectors of the fuzzy ART system are updated using weighted average of the input vector and the weight vector itself, and the bottom-up weight vectors are updated using fuzzy AND operation between the updated top-down weitht vector and bottom-up weight vector itself. The weighted average prevents the excessive update of the weight vectors and the fuzzy AND operation renders the learning fast and stble. Simulation results show that the proposed method reduces the generation of spurious categories and increases the recognition ratio in the noisy input environment.

• 한국지능시스템학회논문지
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• 제7권2호
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• pp.34-42
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• 1997

본 논문은 산업분야의 군사적으로 많이 사용되고 있는 planar shape의 인식을 스펙트럼 분석기를 이용하여 FFT 스펙트럼으로부터 추출된 3차원 특징 벡터와 신경회로망인 fuzzy ARTMAP을 이용하여 시도되었다. 외곽선 정보를 추출하여 이를 원점으로 이동시키고 각 경계점들과 원점들과의 유클리드 거리를 구하여 이를 다시 FFT스펙트럼과 스펙트럼 분석기를 통하여 3차원 특징 벡터를 추출하였다. 이 3차원 데이터는 이동, 회전, 크기에 무관한 값으로 fuzzy ARTMAP에 입력값으로 사용하였다. Fuzzy ARTMAP은 두개의 fuzzy ART 모듈을 가지고 있으며 위에서 구한 특징 벡터들에 의해 학습되고 실험되어 진다.본 논문에 포함된 실험은 4개의 비행기와 4개의 산업부품을 이용하여 잡음이 섞인 shape의 인식에 있엇 제시된 방법이 좋은 인식률을 기록함을 보여주고 있다.

• 한국멀티미디어학회논문지
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• 제4권4호
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• pp.333-338
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• 2001

본 논문에서는 이전에 개발된 신경회로망 채널 등화기에서 볼 수 있었던 구조의 복잡성 및 많은 학습시간의 소요 등과 같은 단점을 극복하고자 퍼지-ARTMAP 신경망을 이용하여 채널 등화기를 구성하였다. 제안된 퍼지-ARTMAP 채널 등화기는 다른 형태의 신경망을 이용한 등화기에서는 찾아 볼 수 없는 빠르고 쉬운 학습 능력을 갖고 있다. 즉, 등화기 구성에 필요한 파라미터의 수가 적으며 지역적 최소값에 빠질 우려 없이 각 계층간의 초기 연결강도를 지정할 수 있을 뿐만 아니라 기존의 학습된 데이터를 재학습시킬 필요 없이 새로운 데이터를 단순히 추가 학습시킬 수 있는 장점 등을 가지고 있다. 본 연구의 시뮬레이션 과정에서는 선형채널에서 발생된 가우시안 잡음을 동반한 이진 신호를 대상으로 퍼지-ARTMAP 채널 등화기의 성능을 LMS 기반의 선형 등화기 및 MLP와 RBF 신경망 등화기와 비교하였으며 퍼지-ARTMAP 등화기가 상대적으로 간단한 구조와 빠른 처리속도를 가짐은 물론 선형등화기로 해결하지 못했던 비선형 문제들도 해결할 수 있음을 보였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권1호
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• pp.82-86
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• 2008

The article presents ARTMAP and Fuzzy BK-Product approach underwater obstacle avoidance for the Autonomous underwater Vehicles (AUV). The AUV moves an unstructured area of underwater and could be met with obstacles in its way. The AUVs are equipped with complex sensorial systems like camera, aquatic sonar system, and transducers. A Neural integrated Fuzzy BK-Product controller, which integrates Fuzzy logic representation of the human thinking procedure with the learning capabilities of neural-networks (ARTMAP), is developed for obstacle avoidance in the case of unstructured areas. In this paper, ARTMAP-Fuzzy BK-Product controller architecture comprises of two distinct elements, are 1) Fuzzy Logic Membership Function and 2) Feed-Forward ART component. Feed-Forward ART component is used to understanding the unstructured underwater environment and Fuzzy BK-Product interpolates the Fuzzy rule set and after the defuzzyfication, the output is used to take the decision for safety direction to go for avoiding the obstacle collision with the AUV. An on-line reinforcement learning method is introduced which adapts the performance of the fuzzy units continuously to any changes in the environment and make decision for the optimal path from source to destination.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권7호
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• pp.2683-2702
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• 2015

A Content-based Image Retrieval (CBIR) system employs visual features rather than manual annotation of images. The selection of optimal features used in classification of images plays a key role in its performance. Category proliferation problem has a huge impact on performance of systems using Fuzzy Artmap (FAM) classifier. The proposed CBIR system uses a modified version of FAM called Non-Proliferation Fuzzy Artmap (NPFAM). This is developed by introducing significant changes in the learning process and the modified algorithm is evaluated by extensive experiments. Results have proved that NPFAM classifier generates a more compact rule set and performs better than FAM classifier. Accordingly, the CBIR system with NPFAM classifier yields good retrieval.

• 한국지능시스템학회논문지
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• 제5권4호
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• pp.33-40
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• 1995

This paper delas with the detection of freehand forgeries of signatures based on the averaged directional amplitudes of gradient vetor which are related to the overall shape of the handwritten signature and fuzzy ARTMAP neural network classifier. In the first step, signature images are extracted from the background by a process involving noise reduction and automatic thresholding. Next, twelve directional amplitudes of gradient vector for each pixel on the signature line are measure and averaged through the entire signature image. With these twelve averaged directional gradient amplitudes, the fuzzy ARTMAP neural network is trained and tested for the detection of freehand forgeries of singatures. The experimental results show that the fuzzy ARTMAP neural network cna lcassify a signature whether genuine or forged with greater than 95% overall accuracy.

• 한국통신학회논문지
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• 제37권3A호
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• pp.172-178
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• 2012

This paper studies the application of a fuzzy-ARTMAP (FAM) neural network to multi-user detector (MUD) for direct sequence (DS)-code division multiple access (CDMA) system. This method shows new solution for solving the problems, such as complexity and long training, which is found when implementing the previously developed neural-basis MUDs. The proposed FAM based MUD is fast and easy to train and includes capabilities not found in other neural network approaches; a small number of parameters, no requirements for the choice of initial weights, automatic increase of hidden units, no risk of getting trapped in local minima, and the capabilities of adding new data without retraining previously trained data. In simulation studies, binary signals were generated at random in a linear channel with Gaussian noise. The performance of FAM based MUD is compared with other neural net based MUDs in terms of the bit error rate.