• International Journal of Fuzzy Logic and Intelligent Systems
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• 제11권4호
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• pp.217-222
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• 2011

Genetic algorithms are one of the most important methods used to solve the Traveling Salesman Problem. Therefore, many researchers have tried to improve the Genetic Algorithm by using different methods and operations in order to find the optimal solution within reasonable time. This paper intends to find a new approach that adopts an improved genetic algorithm to solve the Traveling Salesman Problem, and compare with the well known heuristic method, namely, Kohonen Self-Organizing Map by using different data sets of symmetric TSP from TSPLIB. In order to improve the search process for the optimal solution, the proposed approach consists of three strategies: two separate tour segments sets, the improved crossover operator, and the improved mutation operator. The two separate tour segments sets are construction heuristic which produces tour of the first generation with low cost. The improved crossover operator finds the candidate fine tour segments in parents and preserves them for descendants. The mutation operator is an operator which can optimize a chromosome with mutation successfully by altering the mutation probability dynamically. The two improved operators can be used to avoid the premature convergence. Simulation experiments are executed to investigate the quality of the solution and convergence speed by using a representative set of test problems taken from TSPLIB. The results of a comparison between the new approach using the improved genetic algorithm and the Kohonen Self-Organizing Map show that the new approach yields better results for problems up to 200 cities.

• Journal of Biosystems Engineering
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• 제27권3호
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• pp.227-234
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• 2002

This study was conducted to nondestructively estimate the volumetric information of peach and pit and to visualize the 3D information of internal structure from magnetic resonance imaging(MRI) data. Bruker Biospec 7T spectrometer operating at a proton reosonant frequency of 300 MHz was used for acquisition of MRI data of peach. Image processing algorithms and visualization techniques were implemented by using MATLAB (Mathworks) and Visualization Toolkit(Kitware), respectively. Thresholding algorithm and Kohonen's self organizing map(SOM) were applied to MRI data fur region segmentation. Volumetric information were estimated from segemented images and compared to the actual measurements. The average prediction errors of peach and pit volumes were 4.5%, 26.1%, respectively for the thresholding algorithm. and were 2.1%, 19.9%. respectively for the SOM. Although we couldn't get the statistically meaningful results with the limited number of samples, the average prediction errors were lower when the region segmentation was done by SOM rather than thresholding. The 3D visualization techniques such as isosurface construction and volume rendering were successfully implemented, by which we could nondestructively obtain the useful information of internal structures of peach.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 D
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• pp.1130-1132
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• 1997

This paper proposed an application of artificial neural networks to security assessment(SA) in power system. The SA is a important factor in power system operation, but conventional techniques have not achieved the desired speed and accuracy. Since the SA problem involves classification, pattern recognition, prediction, and fast solution, it is well suited for Kohonen neural network application. Self organizing feature map(SOFM) algorithm in this paper provides two dimensional multi maps. The evaluation of this map reveals the significant security features in power system. Multi maps of multi prototype states are proposed for enhancing the versatility of SOFM neural network to various operating state.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1994년도 FIFTH WESTERN PACIFIC REGIONAL ACOUSTICS CONFERENCE SEOUL KOREA
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• pp.1046-1051
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• 1994

It is known that SOFM has the property of effectively creating topographically the organized map of various features on input signals, SOFM can effectively be applied to the recognition of Korean phonemes. However, is isn't guaranteed that the network is sufficiently learned in SOFM algorithm. In order to solve this problem, we propose the learning algorithm combined with the conventional K-means clustering algorithm in fine-tuning stage. To evaluate the proposed algorithm, we performed speaker dependent recognition experiment using six phoneme classes. Comparing the performances of the Kohonen's algorithm with a proposed algorithm, we prove that the proposed algorithm is better than the conventional SOFM algorithm.

• 한국정보과학회논문지:소프트웨어및응용
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• 제35권9호
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• pp.547-553
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• 2008

본 논문에서는 2 단계 서류 레이아웃 분할 방법을 제안한다. 서류 분할의 1 차 단계는 top-down 계열의 영역 추출로서 모폴로지 기반의 거리 함수를 사용하여 주어진 영상 데이타를 사각형 영역들로 분할한다. 거리 사상 함수를 통한 예비 결과는 성능 개선을 위한 2 차 단계의 입력 변수로 작용한다. 서류 분할의 2차 단계로서 기계 학습 이론을 적용한다. 통계 모델을 따르는 RBF 신경망을 선택하였고, 은닉 층의 설계를 위해 코호넨 네트워크의 자기 조직화 성격을 활용한 데이타 군집화 기법을 기반으로 하였다. 본 논문에서는 300개의 영상에서 추출된 영역 데이타를 통해 학습된 신경망이 1차 단계에서 도출된 예비 결과를 개선함을 연구 결과로 제시하였다.

• 경영과학
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• 제21권3호
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• pp.55-69
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• 2004

This study analyzes customer buying-behavior patterns in a department store as time goes on, and predicts moving patterns of its customers. Through them, it suggests in this paper short-term and long-term marketing promotion strategies. RFM techniques are utilized for customer segmentation. Customers are clustered by using the Kohonen's Self Organizing Map as a method of data mining techniques. Then C5.0, a decision tree analysis technique, is used to predict moving patterns of customers. Using real world data, this study evaluates the prediction accuracy of predictive models.

• 대한전기학회논문지
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• 제39권9호
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• pp.985-996
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• 1990

An inverse kinematic solution of a robot manipulator using multilayer perceptrons is proposed. Neural networks allow the solution of some complex nonlinear equations such as the inverse kinematics of a robot manipulator without the need for its model. However, the back-propagation (BP) learning rule for multilayer perceptrons has the major limitation of being too slow in learning to be practical. In this paper, a new algorithm named Dynamically Reconfiguring BP is proposed to improve its learning speed. It uses a modified version of Kohonen's Self-Organizing Feature Map (SOFM) to partition the input space and for each input point, select a subset of the hidden processing elements or neurons. A subset of the original network results from these selected neuron which learns the desired mapping for this small input region. It is this selective property that accelerates convergence as well as enhances resolution. This network was used to learn the parity function and further, to solve the inverse kinematic problem of a robot manipulator. The results demonstrate faster learning than the BP network.

• 전자공학회논문지B
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• 제28B권9호
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• pp.739-746
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• 1991

This paper develops a modified multilayer perceptron architecture which speeds up learning as well as the net's mapping accuracy. In Phase I, a cluster partitioning algorithm like the Kohonen's self-organizing feature map or the leader clustering algorithm is used as the front end that determines the cluster to which the input data belongs. In Phase II, this cluster selects a subset of the hidden layer nodes that combines the input and outputs nodes into a subnet of the full scale backpropagation network. The proposed net has been applied to two mapping problems, one rather smooth and the other highly nonlinear. Namely, the inverse kinematic problem for a 3-link robot manipulator and the 5-bit parity mapping have been chosen as examples. The results demonstrate the proposed net's superior accuracy and convergence properties over the original backpropagation network or its existing improvement techniques.

• 전자공학회논문지S
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• 제35S권5호
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• pp.75-82
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• 1998

Conventional vector quantization (VQ) codebook design methods have several drawbacks such as edge degradation and high computational complexity. In this paper, we first made activity coordinates from the horizonatal and the vertical activity of the input block. Then it is mapped on the 2-dimensional interconnected codebook, and the codebook is designed using kohonen self-organizing map (KSFM) learning algorithm after the search of a codevector that has the minumum distance from the input vector in a small window, centered by the mapped point. As the serch area is restricted within the window, the computational amount is reduced compared with usual VQ. From the resutls of computer simulation, proposed method shows a better perfomance, in the view point of edge reconstruction and PSNR, than previous codebook training methods. And we also obtained a higher PSNR than that of classified vector quantization (CVQ).

• 대한전기학회논문지:전력기술부문A
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• 제50권4호
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• pp.172-179
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• 2001

A component based method out of load modeling is to aggregate component load model according to the composition rate of each component load at load bus based on the circuit theory. But the most of component loads respond complex nonlinear characteristics respect to voltage and frequency variation due to the control techniques and semiconductor elements applied to component load. It needs to verify this approach through actual experiment of the aggregation of component load even if it can be down. To identify this aggregation method well known, this paper is proposed the classifying method of component load characteristics for component loads to group by quantitative analysis. The component load characteristics were divided into several types by KSOM (kohonen self organizing map), which can classify multi-dimension vector, component load pattern, into two-dimension vector. Some ambiguous cases happened from KSOM were classified by the proposed closing degree.