• 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.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.772-775
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• 2006

Muilti-dimensional feature vector matching algorithm uses multiple features such as intensity, gradient, variance, first or second derivative of a pixel to find correspondence pixels in stereo images. In this paper, we proposed a new method for adjusting automatically the weight of feature in multi-dimensional feature vector matching considering sharpeness of a pixel in feature vector distance curve. The sharpeness consists of minimum and maximum vector distances of a small window mask. In the experiment we used IKONOS satellite stereo imagery and obtained accurate matching results comparable to the manual weight-adjusting method.

• 전자공학회논문지B
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• 제32B권10호
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• pp.1358-1363
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• 1995

This paper presents a new learning method for a noise tolerant Fuzzy ART. In the conventional Fuzzy ART, the top-down and bottom-up weight vectors have the same value. They are updated by a fuzzy AND operation between the input vector and the current value of the top-down or bottom- up weight vectors. However, it can not prevent the abrupt change of the weight vector and can not achieve good performance for a noisy input vector. To solve the problems, we updated using the weighted sum of the input vector and the current value of the top-down vector. To achieve stability, the bottom-up weight vector is updated using the fuzzy AND operation between the newly learned top-down vector and the current value of the bottom-up vector. Computer simulations show that the proposed method prominently resolves the category proliferation problem without increasing the training epoch for stabilization in noisy environments.

• 한국컴퓨터정보학회논문지
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• 제16권5호
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• pp.101-107
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• 2011

본 논문에서는 적응 배열 안테나 시스템에서 간섭 및 재밍 신호를 제거하기 위해서 최적 가중 벡터를 연구하였다. 최적 가중 벡터는 선형 제한 조건에서 최소 분산 알고리즘과 비용함수를 적용시켜 구하였고, 목표물의 신호를 정확히 추정하였다. 적응 배열 안테나 시스템은 간섭 및 재머전력을 감소시키고 신호대 잡음비를 향상시키는 시스템이다. 적응 배열 안테나 시스템은 각 안테나 배열 소자의 출력이 탭(Tap)을 거쳐 지연되고 각 탭에 복소 가중치가 곱해져서 최종적으로 하나의 복합신호를 만든다. 최적의 가중치를 구하기 위해서 본 논문에서는 입력 공분산 행렬의 최적 가중벡터를 이용하였다. 본 논문에서 제안된 알고리즘으로 모의 실험한 결과 분해능은 $3^{\circ}$이하로 향상되었으며 부엽은 약 10 dB 감소하였음을 입증하였다.

• 대한전자공학회논문지SP
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• 제39권4호
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• pp.462-469
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• 2002

벡터 부호책 설계에 사용되는 기존 K-means 알고리즘은 모든 학습반복에서 고정된 가중치를 적용하는데 반해 제안된 방법은 학습반복마다 가변되는 가중치를 적용한다. 초기 학습반복에서는 새로운 부호벡터를 얻기 위해 수렴영역을 벗어나는 2 이상의 가중치를 사용하고, 이 값이 클수록 가변 가중치를 적용하는 학습반복을 줄임으로써 우수한 부호책을 설계할 수 있다. 초기 부호책 설계에 사용되는 미세분할 방법을 개선하기 위하여 소속 학습벡터와 대표벡터간의 오차를 줄이는 방법을 사용한다. 즉 자승오차가 최대인 대표벡터를 제외시키고 최소인 대표벡터를 미세분할함으로써 초기 부호벡터로 대체될 보다 적절한 대표벡터를 얻을 수 있다.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 추계학술대회
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• pp.817-822
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• 2004

This paper provides a global path planning method using self-organizing feature map which is a method among a number of neural network. The self-organizing feature map uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. On the other hand, the modified method in this research uses a predetermined initial weight vectors, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. According to simulation results one can conclude that the modified neural network is useful tool for the global path planning problem of a mobile robot.

• 한국정밀공학회지
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• 제23권3호
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• pp.94-101
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• 2006

A global path planning method using self-organizing feature map which is a method among a number of neural network is presented. The self-organizing feature map uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are moved toward the input vector On the other hand, the modified method in this research uses a predetermined initial weight vectors of 1-dimensional string and 2-dimensional mesh, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are moved toward the input vector. According to simulation results one can conclude that the modified neural network is useful tool for the global path planning problem of a mobile robot.

• 한국정밀공학회지
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• 제25권4호
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• pp.69-76
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• 2008

The self-organizing feature map(SOFM) among a number of neural network uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are moved toward the input vector. On the other hand, the modified method in this research uses a predetermined initial weight vectors of the 1-dimensional string, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are move toward the opposite direction of input vector. According to simulation results one can conclude that the method using string and the modified neural network is useful tool to mobile robot for the global path planning.

• 제어로봇시스템학회논문지
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• 제12권5호
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• pp.473-479
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• 2006

A global path planning algorithm using modified self-organizing feature map(SOFM) which is a method among a number of neural network is presented. The SOFM uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. On the other hand, the modified method in this research uses a predetermined initial weight vectors of the 2-dimensional mesh, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are move toward the opposite direction of input vector. According to simulation results one can conclude that the modified neural network is useful tool for the global path planning problem of a mobile robot.

• 대한수학회보
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• 제58권2호
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• pp.507-514
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• 2021

Vector-valued Eisenstein series of weight 3/2 are often not holomorphic. In this paper we prove that, for an even lattice Ḻ, if there exists an odd prime p such that Ḻ is local p-maximal and the determinant of Ḻ is divisible by p2, then the Eisenstein series of weight 3/2 attached to the discriminant form of Ḻ is holomorphic.