• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2383-2385
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• 2000

It is proposed a new algorithm for a neural network adaptive tracking control scheme to improve performance in this paper. In supervisory control scheme, the upper and lower bound of the parameters are directly estimated by using RBF neural network without their information, and the weighting parameters of the control input are adjusted on-line by adaptation laws. As a result, the proposed algorithm assured that the output errors go to zero without relation to existing minimum approximation errors and disturbances. The effectiveness of the proposed algorithm is demonstrated through the simulation of one-link rigid robotics manipulator.

• ETRI Journal
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• v.29 no.3
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• pp.402-404
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• 2007

One of the major disadvantages of the orthogonal frequency division multiplexing system is high peak-to-average power ratio (PAPR). Selected mapping (SLM) is an efficient distortionless PAPR reduction scheme which selects the minimum PAPR sequence from a group of independent phase rotated sequences. However, the SLM requires explicit side information and a large number of IFFT operations. In this letter we investigate a novel PAPR reduction method based on the radial basis function network and SLM.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.200-200
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• 2000

Highly nonlinear dynamical systems are easily identified using neural networks. When disturbances are included in the learning data set Int system modeling, modeling process will be poorly performed. Since the radial basis functions in the radial basis function network(RBFN) are centered at the points specified by the weights, RBF networks are robust for approximating the process including the narrow-band disturbances deviating significantly from the regular signals. To exclude(filter) these disturbances, a robust algorithm for system identification, based on the RBFN, is proposed. The performance of system identification excluding disturbances is investigated and compared with the one including disturbances.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.3
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• pp.79-85
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• 2001

We obtain a solution of inverse kinematic of 3 axis manipulator by using a self-organizing neral network(SONN) with a fuzzy compensator. The self-organizing neural network using the gaussian potential function as the activation function has one hidden layer in the first learning time. The network obtains the optimal number of node by increasing the number of hidden layer node through the learning, and the fuzzy compensator has the optimal loaming rate of neutral network. In this results, we can confirmed that the learning rate is improved and the rapid convergence to the steady-state.

• The KIPS Transactions:PartB
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• v.10B no.4
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• pp.443-450
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• 2003

In this paper, we proposed the novel hierarchical algorithm for the recognition of English calling cards that processes multiresolution images of calling cards hierarchically to extract individual characters and recognizes the extracted characters by using the enhanced neural network method. The hierarchical recognition algorithm generates multiresolution images of calling cards, and each processing step in the algorithm selects and processes the image with suitable resolution for lower processing overhead and improved output. That is, first, the image of 1/3 times resolution, to which the horizontal smearing method is applied, is used to extract the areas including only characters from the calling card image, and next, by applying the vertical smearing and the contour tracking masking, the image of a half time resolution is used to extract individual characters from the character string areas. Lastly, the original image is used in the recognition step, because the image includes the morphological information of characters accurately. And for the recognition of characters with diverse font types and various sizes, the enhanced RBF network that improves the middle layer based on the ART1 was proposed and applied. The results of experiments on a large number of calling card images showed that the proposed algorithm is greatly improved in the performance of character extraction and recognition compared with the traditional recognition algorithms.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.7
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• pp.1320-1326
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• 2010

In this paper, we develop a design methodology of granular-based neurocomputing networks realized with the aid of the clustering techniques. The objective of this paper is modeling and evaluation of approximation and generalization capability of the Linear-Type Superconducting Power Supply (LTSPS). In contrast with the plethora of existing approaches, here we promote a development strategy in which a topology of the network is predominantly based upon a collection of information granules formed on a basis of available experimental data. The underlying design tool guiding the development of the granular-based neurocomputing networks revolves around the Fuzzy C-Means (FCM) clustering method and the Radial Basis Function (RBF) neural network. In contrast to "standard" Radial Basis Function neural networks, the output neuron of the network exhibits a certain functional nature as its connections are realized as local linear whose location is determined by the membership values of the input space with the aid of FCM clustering. To modeling and evaluation of performance of the linear-type superconducting power supply using the proposed network, we describe a detailed characteristic of the proposed model using a well-known NASA software project data.

• Journal of KIISE:Software and Applications
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• v.29 no.3
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• pp.145-155
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• 2002

In content-based image retrieval (CBIR), most conventional approaches assume a linear relationship between different features and require users themselves to assign the appropriate weights to each feature. However, the linear relationship assumed between the features is too restricted to accurately represent high-level concepts and the intricacies of human perception. In this paper, a neural network-based image retrieval (NNIR) model is proposed. It has been developed based on a human-computer interaction approach to CBIR using a radial basis function network (RBFN). By using the RBFN, this approach determines the nonlinear relationship between features and it allows the user to select an initial query image and search incrementally the target images via relevance feedback so that more accurate similarity comparison between images can be supported. The experiment was performed to calculate the level of recall and precision based on a database that contains 1,015 images and consists of 145 classes. The experimental results showed that the recall and level of the proposed approach were 93.45% and 80.61% respectively, which is superior than precision the existing approaches such as the linearly combining approach, the rank-based method, and the backpropagation algorithm-based method.

• Journal of Intelligence and Information Systems
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• v.4 no.2
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• pp.23-34
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• 1998

Travel time forecasting, especially public bus travel time forecasting in urban areas, is a difficult and complex problem which requires a prohibitively large computation time and years of experience. As the network of target area grows with addition of streets and lanes, computational burden of the forecasting systems exponentially increases. Even though the travel time between two neighboring intersections is known a priori, it is still difficult, if not impossible, to compute the travel time between every two intersections. For the reason, previous approaches frequently have oversimplified the transportation network to show feasibilities of the problem solving algorithms. In this paper, forecasting of the travel time between every two intersections is attempted based on travel time data between two neighboring intersections. The time stamps data of public buses which recorded arrival time at predetermined bus stops was extensively collected and forecast. At first, the time stamp data was categorized to eliminate white noise, uncontrollable in forecasting, based on wavelet conversion. Then, the radial basis neural networks was applied to remaining data, which showed relatively accurate results. The success of the attempt was confirmed by the drastically reduced relative error when the nodes between the target intersections increases. In general, as the number of the nodes between target intersections increases, the relative error shows the tendency of sharp increase. The experimental results of the novel approaches, based on wavelet conversion and neural network teaming mechanism, showed the forecasting methodology is very promising.

• Journal of Distribution Research
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• v.10 no.3
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• pp.1-14
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• 2005

It is important to control performance and a Sustainable Collaboration (SC) for the successful Supply Chain Management (SCM). This research developed a control model which analyzed SCM performances based on a Balanced Scorecard (ESC) and an SC using Support Vector Machine (SVM). 108 specialists of an SCM completed the questionnaires. We analyzed experimental data set using SVM. This research compared the forecasting accuracy of an SCMSC through four types of SVM kernels: (1) linear, (2) polynomial (3) Radial Basis Function (REF), and (4) sigmoid kernel (linear > RBF > Sigmoid > Polynomial). Then, this study compares the prediction performance of SVM linear kernel with Artificial Neural Network. (ANN). The research findings show that using SVM linear kernel to forecast an SCMSC is the most outstanding. Thus SVM linear kernel provides a promising alternative to an SC control level. A company which pursues an SCM can use the information of an SC in the SVM model.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.99-106
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• 2015

In this study, we propose Radial basis function Neural Network(RBFNN) using Recursive Weighted Least Square Estimation(RWLSE) to effectively deal with big data class meteorological radar data. In the condition part of the RBFNN, Fuzzy C-Means(FCM) clustering is used to obtain fitness values taking into account characteristics of input data, and connection weights are defined as linear polynomial function in the conclusion part. The coefficients of the polynomial function are estimated by using RWLSE in order to cope with big data. As recursive learning technique, RWLSE which is based on WLSE is carried out to efficiently process big data. This study is experimented with both widely used some Machine Learning (ML) dataset and big data obtained from meteorological radar to evaluate the performance of the proposed classifier. The meteorological radar data as big data consists of precipitation echo and non-precipitation echo, and the proposed classifier is used to efficiently classify these echoes.