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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 5, Issue 4 - Dec 1995
Volume 5, Issue 3 - Sep 1995
Volume 5, Issue 2 - Jun 1995
Volume 5, Issue 1 - Mar 1995
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On-line Learnign control of Nonlinear Systems Usig Local Affine Mapping-based Networks
Chio, Jin-Young ; Kim, Dong-Sung ;
Journal of Korean Institute of Intelligent Systems, volume 5, issue 3, 1995, Pages 3~10
This paper proposedan on-line learning controller which can be applied to nonlinear systems. The proposed on-line learning controller is based on the universal approximation by the local affine mapping-based neural networks. It has self-organizing and learning capability to adapt itself to the new environment arising from the variation of operating point of the nonlinear system. Since the learning controller retains the knowledge of trained dynamics, it can promptly adapt itself to situations similar to the previously experienced one. This prompt adaptability of the proposed control system is illustrated through simulations.
A Survey on the Fuzzy Control Systems with Learning/Adaptation Capability
Journal of Korean Institute of Intelligent Systems, volume 5, issue 3, 1995, Pages 11~35
In this paper the fuzzy extension for the classical engineering mechanics problems is studied. The governing differential equation is derived for the buckling loads of the columns with uncertain mediums: the their own weight and the flexural rigidity. The columns with one typical end constraint(hinged1 clarnped/free) and the other finite rotational spring with fuzzy constant are considered in numerical examples. The vertex method is used to evaluate the fuzzy functions. The Runge-Kutta method and Determinant Search method are used to solve the differential equation and determine the buckling loads, respectively. The membership functions of the buckling load are calculated. The index of fuzziness to quantitatively describe the propagation of fuzziness is defined. According to the fuzziness of governing factors, the varlation of index of fuzziness for buckling load is investigated, and the sensitivity for the end constraints is analyzed.
Position/Force Control of Robotic Manipulator with Fuzzy Compensation
Journal of Korean Institute of Intelligent Systems, volume 5, issue 3, 1995, Pages 36~51
An approach to robot hybrid position/force control, which allows force manipulations to be realized without overshoot and overdamping while in the presence of unknown environment, is given in this paper. The manin idea is to used dynamic compensation for known robot parts and fuzzy compensation for unknown environment so as to improve system performance. The fuzzy compensation is implemented by using rule based fuzzy approach to identify the unknown environment. The establishment of proposed control system consists of following two stages. First, similar to the resovled acceleration control method, dynamic compensation and PD control based on known robot dynamics, kinematics and estimated environment stiffness is introduced. To avoid overshoot the whole control system is constructed with overdamping. In the second stage, the unknown environment stiffness is identified by using fuzzy reasoning, where the fuzzy compensation rules are obtained priori as the expression of the relationship betweenenvironment stiffness and system. Based on the simulation result, comparison between cases with or without fuzzy identifications are given, which illustrate the improvement achieced.
Neuro-Fuzzy Algorithm for Nuclear Reactor Power Control : Part I
Chio, Jung-In ; Hah, Yung-Joon ;
Journal of Korean Institute of Intelligent Systems, volume 5, issue 3, 1995, Pages 52~63
A neuro-fuzzy algorithm is presented for nuclear reactor power control in a pressurized water reactor. Automatic reacotr power control is complicated by the use of control rods because of highly nonlinear dynamics in the axial power shape. Thus, manual shaped controls are usually employed even for the limited capability during the power maneuvers. In an attempt to achieve automatic shape control, a neuro-fuzzy approach is considered because fuzzy algorithms are good at various aspects of operator's knowledge representation while neural networks are efficinet structures capable of learning from experience and adaptation to a changing nuclear core state. In the proposed neuro-fuzzy control scheme, the rule base is formulated based ona multi-input multi-output system and the dynamic back-propagation is used for learning. The neuro-fuzzy powere control algorithm has been tested using simulation fesponses of a Korean standard pressurized water reactor. The results illustrate that the proposed control algorithm would be a parctical strategy for automatic nuclear reactor power control.
A Study on Development of a Fuzzy Tuner for Tuning Gains of a PI Contorller
Journal of Korean Institute of Intelligent Systems, volume 5, issue 3, 1995, Pages 64~72
This paper proposes how to tune the gains of PI controllers in case of gain change in a process control system. Controllers of PI type have been used in industry and the gains of the controllers have been tuned by expert engineers. It, therefore, takes much time and efforts to tune the controllers. It is more difficult to find gains of multi-loop processes. The tuning method of a fuzzy tuner in this paper is developed based on the assumptions that the PI controllers are of analog type and are tuned off-line, and that the characteristic values must be supplied for the tuner. A Tuner using Fuzzy Logic(FLT1 is capable of showing presentlpast states of a process control system and finding gains of PI controllers. The verfication of the FLT is shown by various experiments.
A Design of an Adaptive Fuzzy controller for the Tokamak Fusion Reactor
Journal of Korean Institute of Intelligent Systems, volume 5, issue 3, 1995, Pages 73~82
The paper demonstrates that an adaptive fuzzy controller can be used effectively for the control of the temperature and density of the Tokarnak fusion recator which is nonlinear and has dynamic uncertainties. The dynamic uncertainties are non-parametric but state dependent. Thus the conventional adaptive nonlinear control methods have difficulties to cope with the problem. The proposed adaptive fuzzy controller can be used as a solution and performs well in a predetermined local space. Simulation result verifies the effectiveness of the scheme.
Fast Learning Algorithms for Neural Network Using Tabu Search Method with Random Moves
Journal of Korean Institute of Intelligent Systems, volume 5, issue 3, 1995, Pages 83~91
A neural network with one or more layers of hidden units can be trained using the well-known error back propagation algorithm. According to this algorithm, the synaptic weights of the network are updated during the training by propagating back the error between the expected output and the output provided by the network. However, the error back propagation algorithm is characterized by slow convergence and the time required for training and, in some situation, can be trapped in local minima. A theoretical formulation of a new fast learning method based on tabu search method is presented in this paper. In contrast to the conventional back propagation algorithm which is based solely on the modification of connecting weights of the network by trial and error, the present method involves the calculation of the optimum weights of neural network. The effectiveness and versatility of the present method are verified by the XOR problem. The present method excels in accuracy compared to that of the conventional method of fixed values.
Modelling Method of Road Choice using Fuzzy Reasoning
Journal of Korean Institute of Intelligent Systems, volume 5, issue 3, 1995, Pages 92~100
Fuzzy reasoning has been applied to analysis of traffic problems on urban arterial road. As the analysis on factors of route choice has been already carried out, its result can be used for construction of the model. Route choice rate estimation by fuzzy reasoning was discussed from its structure and accuracy. The major objective of the study is to introduce some kinds of methods with fuzzy reasoning and to make their feature obvious. First, the production system model is introduced with consideration of reality to actual travel behavior. Second, overlapping areas of fuzzy language function are investigated. Finally, process of fuzzy reasoning was also considered. Five kinds of Fuzzy reasoning are compared to investigate in relation between shapes of membership function and estimation validity.
Handprinted Korean Characters Recognition System bu Using New jaso Decompostion Method
Journal of Korean Institute of Intelligent Systems, volume 5, issue 3, 1995, Pages 101~110
Function Approximation Using Cao s Fuzzy System
Journal of Korean Institute of Intelligent Systems, volume 5, issue 3, 1995, Pages 111~116
Motion Analysis Using Competitive Learning Neural Network and Fuzzy Reasoning
Journal of Korean Institute of Intelligent Systems, volume 5, issue 3, 1995, Pages 117~127
In this paper, we suggest a motion analysis method using ART-I1 competitive learning neural network and fuzzy reasoning by matching the same objects through the consecutive image sequence. we use the size and mean intensity of the region obtained from image segmentation for the region matching by the region and use a ART-I1 competitive learning neural network wh~ch has a learning ability to reflect the topology of the input patterns in order to select characteristic points to describe the shape of a region. Motion vectors for each regions are obtained by matching selected characteristic points. However, the two dimensional image, the projection of the the three dimensional real world, produces fuzziness in motion analysis due to its incompleteness by nature and the error from image segmentation used for extracting information about objects. Therefore, the belief degrees for each regions are calculated using fuzzy reasoning to l-nanipulate uncertainty in motion estimation.