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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 9, Issue 6 - Dec 1999
Volume 9, Issue 5 - Oct 1999
Volume 9, Issue 4 - Aug 1999
Volume 9, Issue 3 - 00 1999
Volume 9, Issue 2 - 00 1999
Volume 9, Issue 1 - 00 1999
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Neural Network Cubes (N-Cubes) for Unsupervised learning in Gray-Scale noise
Lee, Won-Hee ;
Journal of Korean Institute of Intelligent Systems, volume 9, issue 6, 1999, Pages 571~576
We consider a class of auto-associative memories namely N-Cubes (Neural-network Cubes) in which 2-D gray-level images and hidden sinusoidal 1-D wavelets are stored in cubical memories. First we develop a learning procedure based upon the least-squares algorithm, Therefore each 2-D training image is mapped into the associated 1-D waveform in the training phase. Second we show how the recall procedure minimizes errors among the orthogonal basis functions in the hidden layer. As a 2-D images ould be retrieved in the recall phase. Simulation results confirm the efficiency and the noise-free properties of N-Cubes.
Design of an Adaptive Fuzzy Logic Controller using Sliding Mode Scheme
Kwak, Seong-Woo ;
Journal of Korean Institute of Intelligent Systems, volume 9, issue 6, 1999, Pages 577~582
Using a sole input variable simplifies the design process for the fuzzy logic controller(FLC). This is called single-input fuzzy logic controller(SFLC). However it is still deficient in the capability of adapting to the varying operating conditions. We here design a single-input adaptive fuzzy logic controller(AFLC) using a switching function of the sliding mode control. The AFLC can directly incorporate linguistic fuzzy control rules into the controller. Hence some parameters of the membership functions characterizing the linguistic terms of the fuzzy rules can be adjusted by an adaptive law. In the proposed AFLC center values of fuzzy sets are directly adjusted by a fuzzy logic system. We prove that 1) its closed-loop system is globally stable in the sense that all signals involved are bounded and 2)its tracking error converges to zero asymptotically. We perform computer simulation using a nonlinear plant.
Generation of Reusability Decision Algorithm of Object-Oriented Components based on Rough Logic
Journal of Korean Institute of Intelligent Systems, volume 9, issue 6, 1999, Pages 583~590
We propose the reusability decision model of the object-oriented components, which can decide the potentiality of reusability of the object-oriented components actively. Fisrt, we select attributes for the reusability decision of the object-oriented components. Then, we acquire information from the reused components based on the quality measures and criteria proposed by many researches. Lastly, we generate algorithm for the reusability decision of the object-oriented components from the acquired information employing rough set.
Design of a Fuzzy Logic Controller Using Response Surface Methodology
Journal of Korean Institute of Intelligent Systems, volume 9, issue 6, 1999, Pages 591~597
When fuzzy logic controllers which are designed based on plant models and intuitive base are applied to real plants, the control systems may not give satisfactory control results due to the modeling error and the lack of knowledge on the plants. In that case. the controller must be retuned by adjusting the control parameters; this retuning process may require a large number of trial-and-error evaluations and thus much time and cost. In order to resolve these problems, we propose a systematic and efficient procedure for designing a fuzzy logic controller using response surface methodology. First wc select the initial optimal conditions of control parameters using a genetic algorithm, in which a nominal plant model with intrinsic modeling errors is used. And then we determine the tinal optimal conditions of the control parameters using response surface methodology. Computer simulations are performed to verify the capability of the proposed method.
Fuzzy Control of Active Magnetic Bearing System Using a Modified PDC Algorithm
Journal of Korean Institute of Intelligent Systems, volume 9, issue 6, 1999, Pages 598~604
A new fuzzy control algorithm for the control of active magnetic bearing (AMB) systems is proposed in th~sp aper. It combines PDC algorithm based on the LMI design of Joh et al. [4,5] and Mamdani-type control rules using fuzzy singletons to handle the nonlinear characteristics of AMB systems efficiently. They are named fine mode control and coarse mode control, respectively. The coarse mode control yields fast response for large deviation of the rotor and the fine mode control gives desired transient response for small deviation of the rotor. The proposed algorithm is applied to an AMB system to verify the performance of the proposed method. The comparison of the proposed method with a linear controller using a linearized model about the equilibrium point and the PDC algorithm show the superiority of the proposed algorithm.
Design of a Fuzzy-Model-Based Controller for Nonlinear Systems
Journal of Korean Institute of Intelligent Systems, volume 9, issue 6, 1999, Pages 605~614
This paper addresses analysis and design of a class of complex single-input single-output fuzzy control systems. In the proposed method, the fuzzy model, which represents the local dynamic behavior of the given nonlinear system, is utilized to construct the controller. The overall controller consists of the local compensators which compensate the local dynamic linear model and the feed-forward controller which is designed via sliding mode control theory. Therefore, the globally stable fuzzy controller is designed without finding a common Lyapunov matrix. and shows improved perfonnance and tracking results by taking the advantages of fuzzy-model-based control theory and sliding mode control theory. Furthennore, stability analysis is conducted not Ibr the fuzzy model but for the real underlying nonlinear system. Two numerical examples are included to show the effcctiveness and feasibility of the proposed fuzzy control method.
Some Properties of Product Smooth Fuzzy Topological Spaces
Park, Jin-Won ;
Journal of Korean Institute of Intelligent Systems, volume 9, issue 6, 1999, Pages 615~620
We will investigate some properties of product smooth fuzzy topological spaces. We will show that a projection map in product smooth fuzzy topological spaces need not be a fuzzy open map. Furthermore a slice need not be homeomorphic to the coordinate spance which is parallel to it.
A Cluster validity Index for Fuzzy Clustering
Lee, Haiyoung ;
Journal of Korean Institute of Intelligent Systems, volume 9, issue 6, 1999, Pages 621~626
In this paper a new cluster validation index which is heuristic but able to eliminate the monotonically decreasing tendency occurring in which the number of cluster c gets very large and close to the number of data points n is proposed. We review the FCM algorithm and some conventional cluster validity criteria discuss on the limiting behavior of the proposed validity index and provide some numerical examples showing the effectiveness of the proposed cluster validity index.
Cooperative Strategies and Swarm Behavior in Distributed Autonomous Robotic Systems based on Artificial Immune System
Journal of Korean Institute of Intelligent Systems, volume 9, issue 6, 1999, Pages 627~633
In this paper, we propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on immune system in distributed autonomous robotic system (DARS). Immune system is living body's self-protection and self-maintenance system. These features can be applied to decision making of optimal swarm behavior in dynamically changing environment. For applying immune system to DARS, a robot is regarded as a ？3-cell, each environmental condition as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. When the environmental condition (antigen) changes, a robot selects an appropriate behavior strategy (antibody). And its behavior strategy is stimulated and suppressed by other robot using communication (immune network). Finally much stimulated strateby is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and immune network hypothesis, and it is used for decision making of optimal swarm strategy. Adaptation ability of robot is enhanced by adding T-cell model as a control parameter in dynamic environments.
An Implementation of Fuzzy Logic Controller on the Reconfigurable FPGA System
Journal of Korean Institute of Intelligent Systems, volume 9, issue 6, 1999, Pages 634~643
This paper concerns an implementation of f~rry logic controller (FLC') on a reconfigurahle FP
Fuzzy r-derived Sets in Fuzzy Topological Spaces
Kim, Young-Sun ;
Journal of Korean Institute of Intelligent Systems, volume 9, issue 6, 1999, Pages 644~649
in this paper we introduce the notions of fuzzy r-adherent points fuzzy r-accumulation points and fuzzy r-derived sets in fuzzy topological spaces and investigate some of their properties.
A Study on the Convergence of the Evolution Strategies based on Learning
Journal of Korean Institute of Intelligent Systems, volume 9, issue 6, 1999, Pages 650~656
In this paper, we study on the convergence of the evolution strategies by introducing the Lamarckian evolution and the Baldwin effect, and propose a random local searching and a reinforcement local searching methods. In the random local searching method some neighbors generated randomly from each individual are med without any other information, but in the reinforcement local searching method the previous results of the local search are reflected on the current local search. From the viewpoint of the purpose of the local search it is suitable that we try all the neighbors of the best individual and then search the neighbors of the best one of them repeatedly. Since the reinforcement local searching method based on the Lamarckian evolution and Baldwin effect does not search neighbors randomly, but searches the neighbors in the direction of the better fitness, it has advantages of fast convergence and an improvement on the global searching capability. In other words the performance of the evolution strategies is improved by introducing the learning, reinforcement local search, into the evolution. We study on the learning effect on evolution strategies by applying the proposed method to various function optimization problems.