• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.9
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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.

• Proceedings of the KSRS Conference
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• v.2
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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.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.10
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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.

• Journal of the Korea Society of Computer and Information
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• v.16 no.5
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• pp.101-107
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• 2011

The optimum weight vector is studied to remove interference and jamming signals in adaptive array antenna system. The optimum weight vector is calculated to apply a minimum variance algorithm and cost function in linearly constrained conditions, and accurately estimates target's signal. Adaptive array antenna system is the system which improves signal to noise ratio(SNR) and decreases interference and jammer power. Adaptive array antenna system delays at tap output of antenna array element. Each tap finally makes the complex signal of one in multiplier complex weight. In order to obtain optimum's weight calculation, optimum weight vector is used in this paper. After simulation, resolution is increased below $3^{\circ}$, and sidelobe is decreased about 10 dB.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.4
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• pp.462-469
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• 2002

While the conventional K-means algorithms use a fixed weight to design a vector codebook for all learning iterations, the proposed method employs a variable weight for learning iterations. The weight value of two or more beyond a convergent region is applied to obtain new codevectors at the initial learning iteration. The number of learning iteration applying a variable weight must be decreased for higher weight value at the initial learning iteration to design a better codebook. To enhance the splitting method that is used to generate an initial codebook, we propose a new method, which reduces the error between a representative vector and the member of training vectors. The method is that the representative vector with maximum squared error is rejected, but the vector with minimum error is splitting, and then we can obtain the better initial codevectors.

• Proceedings of the KSME Conference
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• 2004.11a
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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.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.3 s.180
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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.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.4
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.5
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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.

• Bulletin of the Korean Mathematical Society
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• v.58 no.2
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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.