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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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Journal DOI :
Korean Institute of Intelligent Systems
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Volume & Issues
Volume 6, Issue 4 - Dec 1996
Volume 6, Issue 3 - Sep 1996
Volume 6, Issue 2 - Jun 1996
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Reducing Memory Requirements of Multidimensional CMAC Problems
Journal of Korean Institute of Intelligent Systems, volume 6, issue 3, 1996, Pages 3~13
In orde to reduce huge memory requirements of multidimensional CMAC problems, building a CMAC system by problem decomposition is investigated. Decomposition is based on resolving a displacement vector in cartesian coordinates into unit vectors that define a few lower-dimensional CMACs in the CMAC system. A CMAC system for an an in verse kinematics problem for a planar manipulator was simulated and the performance of the system was evaluated in terms of training and output quality.
Design of a Fuzzy Classifier by Repetitive Analyses of Multifeatures
Journal of Korean Institute of Intelligent Systems, volume 6, issue 3, 1996, Pages 14~24
A fuzzy classifier which needs various analyses of features using genetic algorithms is proposed. The fuzzy classifier has a simple structure, which contains a classification part based on fuzzy logic theory and a rule generation ation padptu sing genetic algorithms. The rule generation part determines optimal fuzzy membership functions and inclusior~ or exclusion of each feature in fuzzy classification rules. We analyzed recognition rate of a specific object, then added finer features repetitively, if necessary, to the object which has large misclassification rate. And we introduce repetitive analyses method for the minimum size of string and population, and for the improvement of recognition rates. This classifier is applied to three examples of the classification of iris data, the discrimination of thyroid gland cancer cells and the recognition of confusing handwritten and printed numerals. In the recognition of confusing handwritten and printed numerals, each sample numeral is classified into one of the groups which are divided according to the sample structure. The fuzzy classifier proposed in this paper has recognition rates of 98. 67% for iris data, 98.25% for thyroid gland cancer cells and 96.3% for confusing handwritten and printed numeral!;.
Nonlinear Approximation in High-Dimensional Spaces Using Tree-Structured Intelligent Systems
Journal of Korean Institute of Intelligent Systems, volume 6, issue 3, 1996, Pages 25~36
Conventional radial-basis-function networks and fuzzy systems have serious problems in dealing with the non1inea:r approximations on high-dimensional spaces due to the explosive increase of the number of hidden nodes or fuzzy IF-THEN rules. In order to avoid such problems, this paper proposes a tree-structured intelligent system in which semi-local basis functions form its basic elements, and develops a training algorithm for the proposed system based on the modified genetic algorithm and LMS rule. Theoretical analysis is performed on the approximation capability of the proposed system, together with experimental studies which demonstrate the effectiveness of the developed methodology.
Likely Mean Value Theorem of Integrals of Real Mapping Between Fuzzy Bounds
Journal of Korean Institute of Intelligent Systems, volume 6, issue 3, 1996, Pages 37~39
Prarmeter Tuning of Fuzzy Cotroller using Neural Networks System Identifier
Journal of Korean Institute of Intelligent Systems, volume 6, issue 3, 1996, Pages 40~50
By using the neural networks(NN) as system identifier, the on-line self tuning method for fuzzy controller(FC) is proposed. In theis method, the learning of NN is carried out during control operation of FC and the cinsequent parameters of FC is tuned on-line automatically by means of system output errors backpropagated through NN. The Sugeno fuzzy model with constants as consequent parameters is selected for simplifying computation. In procedures of parameter tuning, the gradient descent method is used and the gradient vectors for adjusting the weight of NN are transferred as controller output errors. To evaluate the performance, the proposed method is applied to the inverted pendulum system.
Determination of Optimal Traffic Signal Cycle using Neural Network
Journal of Korean Institute of Intelligent Systems, volume 6, issue 3, 1996, Pages 51~62
Electro sensitive traffic system can not consider passenger car unit, so it causes start up delay time and passenger waiting time. In this paper, it antecedently creates passenger car unit at the bottom intersection using neural network. But, sometimes it can make mistakes due to changes in car weight, car speed, and passing area. Therefore, it consequently reduces the car waiting time and start-up delay time using fuzzy control of feed-back data. Moreover, to prevent spillback, it can adapt control even though upper traffic intersection has a different saturation rate, road length, road slope and road width.
A Study on the Power System Stabilization Using a Neural Network
Journal of Korean Institute of Intelligent Systems, volume 6, issue 3, 1996, Pages 63~72
In this paper, we propose a design technique for a neural network controller and apply it to power system stabilization. Using a learning algorithm of error back propagation that accepts error and change of error as inputs, the momentum learning technique is used by which reduction of learning time is possible for real time control. The related simulation results show that the proposed control techinque is more powerful than the conventional ones for dynamic responses.
Evolutionary Programming-Based Autoplace for Optimal Routing in PCB CAD
Journal of Korean Institute of Intelligent Systems, volume 6, issue 3, 1996, Pages 73~80
In this paper, a new method of finding a sub-optimal solution of an autoplacer which places electrical components autiomatically in PCB CAD tools. The software implementation of the proposed method can be viewed as a new type of floorplan based on evolutionary programming. To solve this problem, three kinds of operators and a fitness function are designed. Computer simulation results demonstrate the usefulness and effectiveness of the proposed scheme in the light of computation time and effort.
An Optimal Cluster Analysis Method with Fuzzy Performance Measures
Journal of Korean Institute of Intelligent Systems, volume 6, issue 3, 1996, Pages 81~88
Cluster analysis is based on partitioning a collection of data points into a number of clusters, where the data points in side a cluster have a certain degree of similarity and it is a fundamental process of data analysis. So, it has been playing an important role in solving many problems in pattern recognition and image processing. For these many clustering algorithms depending on distance criteria have been developed and fuzzy set theory has been introduced to reflect the description of real data, where boundaries might be fuzzy. If fuzzy cluster analysis is tomake a significant contribution to engineering applications, much more attention must be paid to fundamental questions of cluster validity problem which is how well it has identified the structure that is present in the data. Several validity functionals such as partition coefficient, claasification entropy and proportion exponent, have been used for measuring validity mathematically. But the issue of cluster validity involves complex aspects, it is difficult to measure it with one measuring function as the conventional study. In this paper, we propose four performance indices and the way to measure the quality of clustering formed by given learning strategy.
Fuzzy Weakly Implicative Ideals of Bck-Algebras
Journal of Korean Institute of Intelligent Systems, volume 6, issue 3, 1996, Pages 89~93
In this paper, we investigated the relation between the ideals of BCK-algebras and fuzzy ideals. We defined the weakly implicative ideals of BCK-algebras and obtained some properties. We proved some results for the fuzzy weakly implicative ideals of bounded commutative BCK-algebras. We also investigated that the weakly implicative ideals are similar to the fuzzy positive implicative ideals.
Quotient Fuzzy Normed Linear Spaces
Journal of Korean Institute of Intelligent Systems, volume 6, issue 3, 1996, Pages 94~98
The main goal of this paper is to investigate some properties in close connection with the quotient fuzzy norm $ induced by a fuzzy semi-norm $ on a linear space X and the quotient map
where W is a subspace of X.