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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 7, Issue 5 - Dec 1997
Volume 7, Issue 4 - Oct 1997
Volume 7, Issue 3 - Aug 1997
Volume 7, Issue 2 - Jun 1997
Volume 7, Issue 1 - Mar 1997
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A Neuro-contouring controller for High-precision CNC Machine Tools
Journal of Korean Institute of Intelligent Systems, volume 7, issue 5, 1997, Pages 1~7
In this paper, a neuro-contouring control scheme for the high precision machining of CNC machine tools is descrihed. The proposed control system consists of a conventional controller for each axis and an additional neuro-controller. For contouring control, the contour error must be computed during realtime motion, but generally the contour error for nonlinear contours is difficult to he directly computed. We, therefore, propose a new contour error model to approximate real error more exactly, and here we also introduce a cost function for better contouring performance and derive a learning law to adjust the weights of the neuro-controller. The derived learning law guarantees good contouring performance. Usefulness of the proposed control scheme is demonstrated hy computer simulations.
A Job Scheduling Method using Fuzzy Concepts in Multi-Server Environment
Journal of Korean Institute of Intelligent Systems, volume 7, issue 5, 1997, Pages 8~13
In multi-server environment there are many servers which are able to process job requests. So we bave to design a mechanism that selects appropriate servers for processing each job request while maximizing server throughput and minimizing average response time of requests. Conventional methods ac~ opt the load of each server as criteria of server selection. that is, they select a server whose load is not bigger than the others. In this work we propose an approach that uses the degree of server performance, server load and the estimated service time of requested job as guidelines of server selection. We incorporate fuzzification techniques and expert knowledge in this approach. Comparing the performances c~f our approach to that of conventional one, experiments show that the proposed approach provides better performances.
Design of a SMC-type FLC and Its Equivalence
Journal of Korean Institute of Intelligent Systems, volume 7, issue 5, 1997, Pages 14~20
This paper proposes a new design method for the SMC-type FLC and shows that a SMC-type LFC is an extension of the SMC with BL. The conventional SMC-type FLC uses error and change-of-error as inputs of the FLC and generates the absolute value of a switching magnitude. Then, the fuzzy rule table is constructed on a two-dimensional space of the phase plane and has commonly the skew symmetric property. In this paper, we introduce a new variable, signed distance, from the skew symmetric property of the rule table. And thd variable becomes only a fuzzy variable that is used to generate the control input of a SMC-type FLC. that is, we design a new SMC-type FLC that uses a signed distance and a control input as the variables representing the contents of the rule-antecedent and the rule-con-sequent, respectively. Then the number of total rules is reduced and the control performance is almost the same as that of the conventional SMC-type FLC. Additionally, we derive the control law of the ordinary SMC with BL from a new SMC-type FLC. Namely, we show that a FLC is an extension of the SMC with BL.
A Fuzzy Model of Systems using a Neuro-fuzzy Network
Journal of Korean Institute of Intelligent Systems, volume 7, issue 5, 1997, Pages 21~27
Neuro-fuzzy network that combined advantages of the neural network in learning and fuzzy system in inferencing can be used to establish a system model in the design of a controller. In this paper, we presented the neuro-fuzzy system that can be able to generated a linguistic fuzzy model which results in a similar input/output response to the original system. The network was used to model a system. We tested the performance ot the neuro-fuzzy network through computer simulations.
Optimal Configuration of Distribution Network using Genetic Algorithms
Journal of Korean Institute of Intelligent Systems, volume 7, issue 5, 1997, Pages 28~33
This paper presents an application of genetic algorithms for optimal configuration of distribution network. Optimal nehvork is defined to satisfy the condition of load balancing. Three problems are suggested to show the performance of genetic algorithms. To resolve the problems, we propose two different mutation operators, in stead of crossover and mutation operators, which are utilized in both global and local search operations. In addition, arc pattern list is also proposed for an efficient search.
A Study on the Hardware Implementation of Competitive Learning Neural Network with Constant Adaptaion Gain and Binary Reinforcement Function
Journal of Korean Institute of Intelligent Systems, volume 7, issue 5, 1997, Pages 34~45
In this paper, we present hardware implemcntation of self-organizing feature map (SOFM) neural networkwith constant adaptation gain and binary reinforcement function on FPGA. Whereas a tnme-varyingadaptation gain is used in the conventional SOFM, the proposed SOFM has a time-invariant adaptationgain and adds a binary reinforcement function in order to compensate for the lowered abilityof SOFM due to the constant adaptation gain. Since the proposed algorithm has no multiplication operation.it is much easier to implement than the original SOFM. Since a unit neuron is composed of 1adde
tracter and 2 adders, its structure is simple, and thus the number of neurons fabricated onFPGA is expected to he large. In addition, a few control signal: ;:rp sufficient for controlling !he neurons.Experimental results show that each componeni ot thi inipiemented neural network operates correctlyand the whole system also works well.stem also works well.
Adaptive Fuzzy Logic Control Using a Predictive Neural Network
Journal of Korean Institute of Intelligent Systems, volume 7, issue 5, 1997, Pages 46~50
In fuzzy logic control, static fuzzy rules cannot cope with significant changes of parameters of plants or environment. To solve this prohlem, self-organizing fuzzy control. neural-network-hased fuzzy logic control and so on have heen introduced so far. However, dynamically changed fuzzy rules of these schemes may make a fuzzy logic controller Fall into dangerous situations because the changed fuzzy rules may he incomplete or inconsistent. This paper proposes a new adaptive filzzy logic control scheme using a predictivc neural network. Although some parameters of a controlled plant or environment are changed, proposed fuzzy logic controller changes its decision outputs adaptively and robustly using unchanged initial fuzzy rules and the predictive errors generated hy the predictive neural network by on-line learning. Experimental results with a D<' servo-motor position control problem show that propnsed cnntrol scheme is very useful in the viewpoint of adaptability.
A Study on the Position Control of DC servo Motor Usign a Fuzzy Neural Network
Journal of Korean Institute of Intelligent Systems, volume 7, issue 5, 1997, Pages 51~59
In this paper, we perform the position control of a DC servo motor using fuzzy neural controller. We use the Fuzzy controller for the position control, because the Fuzzy controller is designed simpler than other intelligent controller, but it is difficult to design for the triangle membership function format. Therefore we solve the problem using the BP learning method of neural network. The proposed Fuzzy neural network controller has been applied to the position control of various virtual plants. And the DC servo motor position control using the fuzzy neural network controller is performed as a real time experiment.
Fuzzy Inference-based Reinforcement Learning of Dynamic Recurrent Neural Networks
Jun, Hyo-Byung ; Sim, Kwee-Bo ;
Journal of Korean Institute of Intelligent Systems, volume 7, issue 5, 1997, Pages 60~66
This paper presents a fuzzy inference-based reinforcement learning algorithm of dynamci recurrent neural networks, which is very similar to the psychological learning method of higher animals. By useing the fuzzy inference technique the linguistic and concetional expressions have an effect on the controller's action indirectly, which is shown in human's behavior. The intervlas of fuzzy membership functions are found optimally by genetic algorithms. And using recurrent neural networks composed of dynamic neurons as action-generation networks, past state as well as current state is considered to make an action in dynamical environment. We show the validity of the proposed learning algorithm by applying it to the inverted pendulum control problem.
Force Control of Micro Robotic Finger Using Fuzzy Controller
Journal of Korean Institute of Intelligent Systems, volume 7, issue 5, 1997, Pages 67~76
In this paper, a theoretical study is presented for the force control of a miniature robotic manipulator which is driven by a pair of piezo-electric bimorph cells. In the theoretical analysis, one finger is modeled as a flexible cantilevers with a force sensor at the tip and the finger is a solid beam. The robotic finger is used to hold the objects with different stiffness such as an iron block and a living insect and a moving objcet. So it is very important to develop an adequate controller for the holding operation of the finger. The main problems in force controlling are overdamping, overshoot and unknown environment(such as the stiffness of object and unknown plant parameters). So, the main target is propose the new fuzzy compensation for unknown environment and incease the system performance. The fuzzy compensation is implemented by using PI-type fuzzy approach to identified unknown environment. And the result of proposed controller was compared with the conventaional PID and optimal controller.
Development of a Method for the Generator Maintenance Scheduling using Fuzzy Integer Programming
Journal of Korean Institute of Intelligent Systems, volume 7, issue 5, 1997, Pages 77~85
A new technique using integer programming based on fuzzy multi-criteria function is proposed for generator maintenance scheduling. Minimization of maintenance delay cost and maximization of supply reserve power are considered for fuzzy multi-criteria function To obtain an optimal solution for gen- ,:rator maintenance scheduling under fuzzy environment, fuzzy multi-criteria integer programming is )used. In the maintenance scheduling, a characteristic feature of the presented approach is that the crisp conaitraints with uncertainty can be taken into account by using fuzzy set theory and so more flexible solution Ian he obtained. The effectiveness of the proposed approach is demonstrated by the simulation results.