• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.7
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• pp.1320-1326
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• 2010

In this paper, we develop a design methodology of granular-based neurocomputing networks realized with the aid of the clustering techniques. The objective of this paper is modeling and evaluation of approximation and generalization capability of the Linear-Type Superconducting Power Supply (LTSPS). In contrast with the plethora of existing approaches, here we promote a development strategy in which a topology of the network is predominantly based upon a collection of information granules formed on a basis of available experimental data. The underlying design tool guiding the development of the granular-based neurocomputing networks revolves around the Fuzzy C-Means (FCM) clustering method and the Radial Basis Function (RBF) neural network. In contrast to "standard" Radial Basis Function neural networks, the output neuron of the network exhibits a certain functional nature as its connections are realized as local linear whose location is determined by the membership values of the input space with the aid of FCM clustering. To modeling and evaluation of performance of the linear-type superconducting power supply using the proposed network, we describe a detailed characteristic of the proposed model using a well-known NASA software project data.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.3
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• pp.378-382
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• 2011

This paper develops the core competencies of manufacturing workers, analyze the consciousness structure on the core competencies. As the analyzing method of consciousness structure, ISM(Interpretive Structural Modeling) and FSM(Fuzzy Structural Modeling) are used to classify layers and determine the connection state. However, the element of each layer is frequently changed by data. This paper suggests the method with the point of view that the structure is determined basically and the connection state of the structure model is changeable depending on the method; first to determine structure model by ISM, second to determine connection by FSM. By using this method, the objective structure model, analyzing the consciousness on the core competencies of manufacturing workers, is suggested with specialist confirm.

• Industrial Engineering and Management Systems
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• v.15 no.2
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• pp.156-172
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• 2016

Complex and uncertain issues in supply chain result in integrated decision making processes in supply chains. So decentralized (distributed) decision making (DDM) approach is considered as a crucial stage in supply chain planning. In this paper, an uncertain DDM through coordination mechanism is addressed for a multi-product supply chain planning problem. The main concern of this study is comparison of DDM approach with centralized decision making (CDM) approach while some parameters of decision making are assumed to be uncertain. The uncertain DDM problem is modeled through fuzzy mathematical programming in which products' demands are assumed to be uncertain and modeled using fuzzy sets. Moreover, a CDM approach is customized and developed in presence of fuzzy parameters. Both approaches are solved using three fuzzy mathematical optimization methods. Hence, the contribution of this paper can be summarized as follows: 1) proposing a DDM approach for a multi-product supply chain planning problem; 2) Introducing a coordination mechanism in the proposed DDM approach in order to utilize the benefits of a CDM approach while using DDM approach; 3) Modeling the aforementioned problem through fuzzy mathematical programming; 4) Comparing the performance of proposed DDM and a customized uncertain CDM approach on multi-product supply chain planning; 5) Applying three fuzzy mathematical optimization methods in order to address and compare the performance of both DDM and CDM approaches. The results of these fuzzy optimization methods are compared. Computational results illustrate that the proposed DDM approach closely approximates the optimal solutions generated by the CDM approach while the manufacturer's and retailers' decisions are optimized through a coordination mechanism making lasting relationship.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.41-47
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• 2008

Cluster based fuzzy model tree has certain drawbacks to decrease performance of testinB data when over-fitting of training data exists. To reduce the sensitivity of performance due to over-fitting problem, we proposed a modified cluster based fuzzy model tree with node information. To construct model tree, cluster centers are calculated by fuzzy clustering method using all input and output attributes in advance. And then, linear models are constructed at internal nodes with fuzzy membership values between centers and input attributes. In the prediction step, membership values are calculated by using fuzzy distance between input attributes and all centers that passing the nodes from root to leaf nodes. Finally, data prediction is performed by the weighted average method with the linear models and fuzzy membership values. To show the effectiveness of the proposed method, we have applied our method to various dataset. Under various experiments, our proposed method shows better performance than conventional cluster based fuzzy model tree.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.12
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• pp.2681-2690
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• 2010

This paper proposes 3D facial modeling system without using 3D scanner and camera or expensive software. This system enables efficient 3D facial modeling to cost reduction and effort saving for natural facial modeling. It detects edges of component of face using edge detection based on fuzzy logic from any 2D image of front face. It was mapped fitting position with 3D standard face model by detected edge more correctly. Also this system generates 3D face model more easily through floating and flexible control and texture mapping after fitting that connection of control point on detected edge from 2D image and mesh of 3D standard face model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5379-5388
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• 2012

In fuzzy modeling for nonlinear process, the fuzzy rules are typically formed by selection of the input variables, the number of space division and membership functions. The Generation of fuzzy rules for nonlinear processes have the problem that the number of fuzzy rules exponentially increases. To solve this problem, complex nonlinear process can be modeled by generating the fuzzy rules by means of fuzzy division of input space. Therefore, in this paper, rules of non-fuzzy inference systems are generated by partitioning the input space in the scatter form using HCM clustering algorithm. The premise parameters of the rules are determined by membership matrix by means of HCM clustering algorithm. The consequence part of the rules is represented in the form of polynomial functions and the consequence parameters of each rule are identified by the standard least-squares method. And lastly, we evaluate the performance and the nonlinear characteristics using the data widely used in nonlinear process. Through this experiment, we showed that high-dimensional nonlinear systems can be modeled by a very small number of rules.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.480-480
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• 2000

In this paper, we proposed a fuzzy-neuro controller to control the speed of wound rotor induction motor with slip energy recovery. The speed is limited at some range of sub-synchronous speed of the rotating magnetic field. Control speed by adjusting resistance value in the rotor circuit that occurs the efficiency of power are reduced, because of the slip energy is lost when it passes through the rotor resistance. The control system is designed to maintain efficiency of motor. Recently, the emergence of artificial neural networks has made it conductive to integrate fuzzy controllers and neural models for the development of fuzzy control systems, Fuzzy-neuro controller has been designed by integrating two neural network models with a basic fuzzy logic controller. Using the back propagation algorithm, the first neural network is trained as a plant emulator and the second neural network is used as a compensator for the basic fuzzy controller to improve its performance on-line. The function of the neural network plant emulator is to provide the correct error signal at the output of the neural fuzzy compensator without the need for any mathematical modeling of the plant. The difficulty of fine-tuning the scale factors and formulating the correct control rules in a basic fuzzy controller may be reduced using the proposed scheme. The scheme is applied to the control speed of a wound rotor induction motor process. The control system is designed to maintain efficiency of motor and compensate power factor of system. That is: the proposed controller gives the controlled system by keeping the speed constant and the good transient response without overshoot can be obtained.

• Journal of the Korea Society of Computer and Information
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• v.10 no.1 s.33
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• pp.133-140
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• 2005

If a Player car precedes from start to the end or a fuzzy car wins every day during computer or racing games, most players will lose their interest in the games soon after several times. In order to solve this Problem and increase amusement at racing games, the more important thing than anything else is decide the ranking. In this thesis, in order to give amusement In racing games, the researcher made a fuzzy car and made it race with player cars. Because the preceding fuzzy car runs ahead of player cars, it can recognize their behaviors according to change of following player cars' speed and distance, and the fuzzy car changes its memory, but doesn't enforce actual behaviors. If the fuzzy car would make decision, it has to do behaviors to compete the ranking on the basis of the contracts it has memorized under the situation where a timer is awarded. In addition, although an accompanying fuzzy car has different contents of memory, it is operated in the same way as mentioned above. At the time of experiments, the researcher applied the actual value to the test program and drew result for ranking competition. In conclusion, the researcher could confirm that we can have modeling of various behaviors by means of the method using fuzzy logic rather than simple if-then method.

• Journal of the Earthquake Engineering Society of Korea
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• v.9 no.2 s.42
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• pp.7-15
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• 2005

Numerical analysis model is proposed to predict the dynamic behavior of a single-degree-of-freedom structure that is equipped with hybrid base isolation system. Hybrid base isolation system is composed of friction pendulum systems (FPS) and a magnetorheological (MR) damper. A neuro-fuzzy model is used to represent dynamic behavior of the MR damper. Fuzzy model of the MR damper is trained by ANFIS (Adaptive Neuro-Fuzzy Inference System) using various displacement, velocity, and voltage combinations that are obtained from a series of performance tests. Modelling of the FPS is carried out with a nonlinear analytical equation that is derived in this study and neuro-fuzzy training. Fuzzy logic controller is employed to control the command voltage that is sent to MR damper. The dynamic responses of experimental structure subjected to various earthquake excitations are compared with numerically simulated results using neuro-fuzzy modeling method. Numerical simulation using neuro-fuzzy models of the MR damper and FPS predict response of the hybrid base isolation system very well.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.59-66
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• 2010

This paper is proposed an adaptive fuzzy bilinear synchronization design for uncertain $L\ddot{u}$ chaos system. It is assumed that the $L\ddot{u}$ chaos system has unknown parameters. First, The $L\ddot{u}$ chaos system can be reconstructed via TS fuzzy bilinear modeling. We design an adaptive fuzzy bilinear synchronization control scheme based on TS fuzzy bilinear $L\ddot{u}$ chaos system with uncertain parameters. Lyapunov theory is employed to guarantee the stability of error dynamic system between TS fuzzy bilinear $L\ddot{u}$ chaos system and the proposed slave system and to derive the adaptive laws for estimating unknown parameters. Simulation results is given to demonstrate the validity of our proposed synchronization scheme.