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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Journal of Korean Institute of Intelligent Systems
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Korean Institute of Intelligent Systems
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Volume & Issues
Volume 10, Issue 6 - Dec 2000
Volume 10, Issue 5 - Oct 2000
Volume 10, Issue 4 - Aug 2000
Volume 10, Issue 3 - Jun 2000
Volume 10, Issue 2 - Apr 2000
Volume 10, Issue 1 - 00 2000
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Approximate Fuzzy Clustering Based on Density Functions
Journal of Korean Institute of Intelligent Systems, volume 10, issue 4, 2000, Pages 285~292
In general, exploratory data analysis consists of three processes: i) assessment of clustering tendency, ii) cluster analysis, and iii) cluster validation. This analysis method requiring a number of iterations of step ii) and iii) to converge is computationally inefficient. In this paper, we propose a density function-based approximate fuzzy clustering method with a hierachical structure which consosts of two phases: Phase I is a features(i.e., number of clusters and cluster centers) extraction process based on the tendency assessment of a given data and Phase II is a standard FCM with the cluster centers intialized by the results of the Phase I. Numerical examples are presented to show the validity of the proposed clustering method.
Fuzzy Inference of Large Volumes in Parallel Computing Environments
Journal of Korean Institute of Intelligent Systems, volume 10, issue 4, 2000, Pages 293~298
In fuzzy expert systems or database systems that have volumes of fuzzy data or large fuzzy rules, the inference time is much increased. Therefore, a high performance parallel fuzzy computing environment is needed. In this paper, we propose a parallel fuzzy inference mechanism in parallel computing environments. In this, fuzzy rules are distributed and executed simultaneously. The ONE_TO_ALL algorithm is used to broadcast the fuzzy input input vector to the all nodes. The results of the MIN/MAX operations are transferred to the output processor by the ALL_TO_ONE algorithm. By parallel processing of fuzzy or data, the parallel fuzzy inference algortihm extracts effective and achieves and achieves a good speed factor.
Design of a Extended Fuzzy Information Retrieval System using User한s Preference
Journal of Korean Institute of Intelligent Systems, volume 10, issue 4, 2000, Pages 299~303
The goal of the information retrieval system is to search the docments which the user wants to obtain in fast and effiecient way. Many information retrieval models, including boolean models, vector models and fuzzy models based on the trasitional fuzzy set theory, have been proposed to achieve these kinds of objectives. However, the previous models have a limitation on the fact that they do not consider the users' preference in the search of documents. In this paper, we proposed a new extenced fuzzy information retrieval System which can handle the shortcomings of the previous ones. In the proposed model, a new similarity measure was applied in order to calculate the degree among documents, which can expliot the users' preference.
Analyzing the Emotional State EEG by Mutual Information
Journal of Korean Institute of Intelligent Systems, volume 10, issue 4, 2000, Pages 304~309
For understanding the information processing in human brain, we analyze the EEG, a spontaneous electric activity on the scalp of the human. In this paper, we used the mutual information to analyze EEG. The mutual information is used to show the stochastic correlation between signals which are generated in the communication and information theory. The used EEG is evoked by each auditory stimulus in positive and negative emotional states. As a result, we found thet there is some difference at the mutual information in each emotional state.
Implementation of Image Thinning using Threshold Neural Network
Journal of Korean Institute of Intelligent Systems, volume 10, issue 4, 2000, Pages 310~314
This paper proposes a new parallel architecture for extracting the object from binarized images using recurrent linear threshold neural networks. Binary functions are initially obtained from the existing iterative thinning algorithms, and the linear threshold neural threshold neural networks are then synthesized using the MSP term grouping algorithm. Experimental results show that the proposed architectures can be implemented easier than with other existing methods.
Evolutionary Neural Network based on DNA coding method for Time series prediction
Journal of Korean Institute of Intelligent Systems, volume 10, issue 4, 2000, Pages 315~323
In this paper, we propose a method of constructing neural networks using bio-inpired emergent and evolutionary concepts. This method is algorithm that is based on the characteristics of the biological DNA and growth of plants, Here is, we propose a constructing method to make a DNA coding method for production rule of L-system. L-system is based on so-called the parallel rewriting nechanism. The DNA coding method has no limitation in expressing the produlation the rule of L-system. Evolutionary algotithms motivated by Darwinaian natural selection are population based searching methods and the high performance of which is highly dependent on the representation of solution space. In order to verify the effectiveness of our scheme, we apply it one step ahead prediction of Mackey-Glass time series, Sunspot data and KOSPI data.
Speech Enhancement the Neural Network Filer
Journal of Korean Institute of Intelligent Systems, volume 10, issue 4, 2000, Pages 324~329
Forecasting of Real Time Traffic Situation
Journal of Korean Institute of Intelligent Systems, volume 10, issue 4, 2000, Pages 330~337
This paper proposes a new concept of coordinating green this which controls 10 traffic intersection systems. For instance, if we have a baseballs game at 8 pm today, traffic volume toward the baseball game at 8 pm today, traffic volume toward the baseball game will be incr eased 1 hour or 1 hour 30 minutes before the baseball game. at that time we can not pred ict optimal green time Even though there have smart elctrosensitive traffic light system. Therefore, in this paper to improve average vehicle speed and reduce average vehicle waiting time, we created optimal green time using fuzzy rules and neural network. Computer simulation results proved reducing average vehicle waiting time proposed coordinating green time better than electro-sensitive traffic light system. Therefore, in this paper to improvevehicle speed and reduce average vehicle waiting time, we created optiual green time fuzzy rules and neural network. Computer simulation results proved reducing average vehicle waiting time which proposed coordinating green time better than electro-sensitive traffic light system dosen't consider coordinating green time.
Optimal Structure of Wavelet Neural Network Systems using Genetic Algorithm
Journal of Korean Institute of Intelligent Systems, volume 10, issue 4, 2000, Pages 338~342
In order to approximate a nonlinear function, wacelet neural networks combining wacelet theory and neural networks have been proposed as an alternative to conventional multi-layered neural networks. wacelet neural networks provide better approximating performance than conventional neural networks. In this paper, an effective method to construct an optimal wavelet neural network is proposed using genetic alogorithm. Genetic Algorithm is used to determine dilationa and translations of wavelet basic functions of wavelet neural networks. Then, these determined dilations dilations and translations, wavelet neural networks are funther trained by back propagation learning algorithm. The effectiveness of the final network is verified thrifigh the approximation result of a nonlinear function and comparison with conventional neural networks.
Design of Multi-FPNN Model Using Clustering and Genetic Algorithms and Its Application to Nonlinear Process Systems
Journal of Korean Institute of Intelligent Systems, volume 10, issue 4, 2000, Pages 343~350
In this paper, we propose the Multi-FPNN(Fuzzy Polynomial Neural Networks) model based on FNN and PNN(Polyomial Neural Networks) for optimal system identifacation. Here FNN structure is designed using fuzzy input space divided by each separated input variable, and urilized both in order to get better output performace. Each node of PNN structure based on GMDH(Group Method of Data handing) method uses two types of high-order polynomials such as linearane and quadratic, and the input of that node uses three kinds of multi-variable inputs such as linear and quadratic, and the input of that node and Genetic Algorithms(GAs) to identify both the structure and the prepocessing of parameters of a Multi-FPNN model. Here, HCM clustering method, which is carried out for data preproessing of process system, is utilized to determine the structure method, which is carried out for data preprocessing of process system, is utilized to determance index with a weighting factor is used to according to the divisions of input-output space. A aggregate performance inddex with a wegihting factor is used to achieve a sound balance between approximation and generalization abilities of the model. According to the selection and adjustment of a weighting factor of this aggregate abjective function which it is acailable and effective to design to design and optimal Multi-FPNN model. The study is illustrated with the aid of two representative numerical examples and the aggregate performance index related to the approximation and generalization abilities of the model is evaluated and discussed.
Document Ranking Method using Extended Fuzzy Concept Networks in Information Retrieval
Journal of Korean Institute of Intelligent Systems, volume 10, issue 4, 2000, Pages 351~356
The important thing of Information Retrieval System is to satisfy is to satisfy the user's requriement in searching Information Retrieval system ranks documents by weights in document, then Retrieved document context does not consist with given query. This paper proposes a new method of document retrieval based on extended fuzzy concept networks. there are four of fuzzy relationships between concept; fuzzy positive combination, fuzzy negative combination, fuzzy generalization, and fuzzy specilalization. After modeling an extended fuzzy concept network by relation matrix and relevance matrix, we measured similarties.
Design of Robust Fuzzy Controller for Load-Frequency Control of Power Systems Using Intelligent Digital Redesign Technique
Joo, Young-Hoon ; Jeo, Sang-Won ; Kwon, Oh-Sin ;
Journal of Korean Institute of Intelligent Systems, volume 10, issue 4, 2000, Pages 357~367
A new robust load-frequency control methodology is proposed for nonlinear power systems with valve position limits of the governor in the presence of parametric uncertaines. The TSK fuzzy model is adopted and formulated for fuzzy modeling of the nonlinear power system. A sufficient condition of the robust stabilitry is presented in the sense of lyapunov for the TSK model with parametric uncertainties. The intekkigent digital redesign technique for the uncertain power systems is also studied. The effectiveness of the robust digital fuzzy controller disign mothod is demonstrated through a numerical simulation.
Development of Fuzzy Control Algorithm for Multi-Objective Problem using Orthogonal Array and its Applications
Journal of Korean Institute of Intelligent Systems, volume 10, issue 4, 2000, Pages 368~373
In this paper, a control algorthm suitable for multi-objective control is proposed based on the orthogonal array which is normally used in statics and industrial engineering. And a newly defined Nthcertainty factor is suggested, which can effectively exclude the less confident rule. The Nth-certainty factor is defined by the F-values of the ANOVA(analysis of variance) table. It is shown that the algorithm can be successfully adopted to the design of controller for an active magnetic bearing system.
Text filtering by Boosting Linear Perceptrons
O, Jang-Min ; Zhang, Byoung-Tak ;
Journal of Korean Institute of Intelligent Systems, volume 10, issue 4, 2000, Pages 374~378
in information retrieval, lack of positive examples is a main cause of poor performance. In this case most learning algorithms may not characteristics in the data to low recall. To solve the problem of unbalanced data, we propose a boosting method that uses linear perceptrons as weak learnrs. The perceptrons are trained on local data sets. The proposed algorithm is applied to text filtering problem for which only a small portion of positive examples is available. In the experiment on category crude of the Reuters-21578 document set, the boosting method achieved the recall of 80.8%, which is 37.2% improvement over multilayer with comparable precision.
Evolvable Cellular Classifiers for pattern Recognition
Journal of Korean Institute of Intelligent Systems, volume 10, issue 4, 2000, Pages 379~389
A cellular automaton is well-known for self-organizing and dynamic behavions in the filed of artifial life. This paper addresses a new neuronic architecture called an evolvable celluar classifier which evolves with the genetic rules (chromosomes) in the non-uniform cellular automata. An evolvable cellular classifier is primarily based on cellular programming, but its mechanism is simpler becaise it utilizes only mutations for the main genetic operators and resmbles the Hopfield network. Therefore, the desirable bit-patterns could be obtained through evolutionary processes for just one individual agent, As a rusult, an evolvable hardware is derived which is applicable to clessification of bit-string information.