• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.322-324
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• 2005

In this paper, we introduce the advanced Self-Organizing Fuzzy Polynomial Neural Network based on optimized FPN by evolutionary algorithm and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed model gives rise to a structurally and parametrically optimized network through an optimal parameters design available within Fuzzy Polynomial Neuron(FPN) by means of GA. Through the consecutive process of such structural and parametric optimization, an optimized and flexible the proposed model is generated in a dynamic fashion. The performance of the proposed model is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.3
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• pp.57-65
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• 1995

This paper presents a new classified vector quantization (VQ) technique using a neural network model in the transform domain. Prior to designing a codebook, the proposed approach extracts class features from a set of images using self-organizing feature map (SOFM) that has the pattern recognition characteristics and the same as VQ objective. Since we extract the class features from the training images unlike previous approaches, the reconstructed image quality is improved. Moreover, exploiting the adaptivity of the neural network model makes our approach be easily applied to designing a new vector quantizer when the processed image characteristics are changed. After the generalized BFOS algorithm allocates the given bits to each class, codebooks of each class are also generated using SOFM for the maximal reconstructed image quality. In experimental results using monochromatic images, we obtained a good visual quality in the reconstructed image. Also, PSNR is comparable to that of other classified VQ technique and is higher than that of JPEG baseline system.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.10a
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• pp.80-84
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• 2002

In this study, researchers developed the est algorithm for artificial defects in the semic packages and performed to it by pattern recogn technology. For this purpose, this algorithm was I that researcher made software with matlab. The so consists of some procedures including ultrasonic acquistion, equalization filtering, self-organizing backpropagation neural network. self-organizing ma backpropagation neural network are belong to metho neural networks. And the pattern recognition tech has applied to classify three kinds of detective pa semiconductor packages. that is, crack, delaminat normal. According to the results, it was found estimative algorithm was provided the recognition r 75.7%( for crack) and 83.4%( for delamination) 87.2 % ( for normal).

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.4
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• pp.31-36
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• 2001

In this paper we propose a method of pattern classification of the hand movement using EMG signals through Self-organizing feature map. Self-organizing feature map is an artificial neural network which organizes its output neuron through learning and therefore it can classify input patterns. The raw EMG signals become direct input to the Self-organizing feature map. The simulation and experiment results showed the effectiveness of the classification of EMG signal using the Self-organizing feature map.

• Journal of KIISE:Software and Applications
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• v.33 no.4
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• pp.440-448
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• 2006

Self-Organizing Map (SOM) is an unsupervised learning neural network and it is used for preserving the structural relationships in the data without prior knowledge. SOM has been applied in the study of complex problems such as vector quantization, combinatorial optimization, and pattern recognition. This paper proposes a new usage of SOM as a tool for schema transformation hoping to achieve more efficient genetic process. Every offspring is transformed into an isomorphic neural network with more desirable shape for genetic search. This helps genes with strong epistasis to stay close together in the chromosome. Experimental results showed considerable improvement over previous results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.4B
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• pp.399-404
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• 2011

For the underwater wireless sensor networks, we propose the packet transmission method which distinguishes more important packet than others. Because the ocean underwater transmission environments are extremely unstable, we use SON(Self Organizing Network) techniques to adapt to the constantly varying underwater acoustic communication channels and randomly deployed sensor nodes. Especially we suppose two kinds of packets which have different priorities, and through the simulations we show that high priority packets arrive at the source node faster than lower priority packets with a proposed scheme.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.142-145
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• 2003

Many control applications using Neural Network need a priori information about the objective system. But it is impossible to get exact information about the objective system in real world. To solve this problem, several control methods were proposed. Reinforcement learning control using neural network is one of them. Basically reinforcement learning control doesn't need a priori information of objective system. This method uses reinforcement signal from interaction of objective system and environment and observable states of objective system as input data. But many methods take too much time to apply to real-world. So we focus on faster learning to apply reinforcement learning control to real-world. Two data types are used for reinforcement learning. One is reinforcement signal data. It has only two fixed scalar values that are assigned for each success and fail state. The other is observable state data. There are infinitive states in real-world system. So the number of observable state data is also infinitive. This requires too much learning time for applying to real-world. So we try to reduce the number of observable states by classification of states with Self-Organizing Map. We also use neural dynamic programming for controller design. An inverted pendulum on the cart system is simulated. Failure signal is used for reinforcement signal. The failure signal occurs when the pendulum angle or cart position deviate from the defined control range. The control objective is to maintain the balanced pole and centered cart. And four states that is, position and velocity of cart, angle and angular velocity of pole are used for state signal. Learning controller is composed of serial connection of Self-Organizing Map and two Multi-layer Feed-Forward Neural Networks.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37B no.9
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• pp.741-749
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• 2012

Ad Hoc Self-Organizing TDMA (ASO-TDMA) has been proposed as a specification to support the multi-hop data communication service for ships over VHF band. It allows for organizing a multi-hop ad-hoc network in a distributed manner by sharing the radio resources among the ships navigating along the route. In this paper, Markov chain analysis is given to provide the average throughput performance for ASO-TDMA protocol Furthermore, the analytical results are verified with computer simulation, which shows that there exists the optimal transmission rate to maximize the average throughput as the subframe size and the number of ships are varying in each hop region.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.928-931
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• 2002

Network based intrusion detection system is a computer network security tool. In this paper, we present an intrusion detection system based on Self-Organizing Maps (SOM) and Resilient Propagation Neural Network (RPROP) for visualizing and classifying intrusion and normal patterns. We introduce a cluster matching equation for finding principal associated components in component planes. We apply data from The Third International Knowledge Discovery and Data Mining Tools Competition (KDD cup'99) for training and testing our prototype. From our experimental results with different network data, our scheme archives more than 90 percent detection rate, and less than 5 percent false alarm rate in one SYN flooding and two port scanning attack types.

• The Journal of the Acoustical Society of Korea
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• v.13 no.6
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• pp.83-90
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• 1994

In this paper, we propose a new learning algorithm, ASOFM(Adaptive Self Organizing Feature Map), to solve the defects of Kohonen's Self Organiaing Feature Map. Kohonen's algorithm is sometimes stranded on local minima for the initial weights. The proposed algorithm uses an object function which can evaluate the state of network in learning and adjusts the learning rate adaptively according to the evaluation of the object function. As a result, it is always guaranteed that the state of network is converged to the global minimum value and it has a capacity of generalized learning by adaptively. It is reduce that the learning time of our algorithm is about $30\%$ of Kohonen's.