• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2891-2893
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• 2005

In this paper, we introduce a new category of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) that is based on a genetically optimized multi-layer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization. The conventional SOFPNN algorithm leads to a tendency to produce overly complex networks as well as a repetitive computation load by the trial and error method and/or the a repetitive parameter adjustment by designer. In order to generate a structurally and parametrically optimized network, such parameters need to be optimal. In this study, in solving the problems with the conventional SOFPNN, we introduce a new design approach of evolutionary optimized SOFPNN. Optimal parameters design available within FPN (viz. the no. of input variables, the order of the polynomial, input variables, and the no. of membership function) lead to structurally and parametrically optimized network which is more flexible as well as simpler architecture than the conventional SOFPNN. In addition, we determine the initial apexes of membership functions by genetic algorithm.

• KIEE International Transactions on Power Engineering
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• v.3A no.4
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• pp.181-190
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• 2003

This paper presents the design of a fuzzy power system stabilizer (FPSS) using an adaptive evolutionary algorithm (AEA). AEA consists of genetic algorithm (GA) for a global search capability and evolution strategy (ES) for a local search in an adaptive manner when the present generation evolves into the next generation. AEA is used to optimize the membership functions and scaling factors of the FPSS. To evaluate the usefulness of the FPSS, we applied it to a single-machine infinite bus system (SIBS) and a power system simulator at the Korea Electrotechnology Research Institute. The FPSS displays better control performance than the conventional power system stabilizer (CPSS) for a three-phase fault in heavy load, which is used when tuning FPSS. To show the robustness of the FPSS, it is applied with disturbances such as change of mechanical torque and three-phase fault in nominal and heavy load, etc. The FPSS also demonstrates better robustness than the CPSS. Experimental results indicate that the FPSS has good system damping under various disturbances such as one-line to ground faults, line parameter changes, transformer tap changes, etc.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.106-116
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• 1998

A position control algorithm of a flexible manipulator is studied. The proposed algorithm is based on an ACFAC(Automatic Constructed Fuzzy Adaptive Controller) system based on the neural network learning algorithms. The proposed system learns membership functions for input variables using unsupervised competitive learning algorithm and output information using supervised outstar learning algorithm. ACFAC does not need a dynamic modeling of the flexible manipulator. An ACFAC is designed that the end point of the flexible manipulator tracks the desired trajectory. The control input to the process is determined by error, velocity and variation of error. Simulation and experiment results show a robustness of ACFAC compared with the PID control and neural network algorithms.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.451-458
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• 2006

The goal of many researchers in the field of structural engineering is to reduce both damage to building structures and discomfort of their inhabitants during strong motion seismic events. The present paper reports on analytical work conducted with this aim in mind as a prior research of experimental study. A four-story, 6.4 m tall, laboratory model of a building is employed as a example structure. The laboratory structure has graphite epoxy columns and each floor is equipped with a chevron brace that serves to resist inter-story drift with the installation of a magnetorheological (MR) damper. An artificial excitation has been generated with a robust range of seismic characteristics. A series of numerical simulations demonstrates that an optimized fuzzy controller is capable of robust performance for a variety of seismic base motions. Optimization of the fuzzy controller is achieved using multi-objective genetic algorithm(MOGA), i.e. NSGA-II. Multiple objective functions are used in order to reduce both peak and root-means-squared displacement and accelerations at the floor levels of the building.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.10
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• pp.2301-2306
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• 2015

The time to address the exabytes of data has come as the information age accelerates. Big data transfer technology is essential for processing large amounts of data. This paper posits to transfer big data in the optimal conditions by the proposed algorithm for predicting the optimal combination of Pipelining, Concurrency, and Parallelism (PCP), which are major functions of GridFTP. In addition, the author introduced a simple design process of Takagi-Sugeno-Kang (TSK) fuzzy model and designed a model for predicting transfer throughput with optimal combination of Pipelining, Concurrency and Parallelism. Hence, the author evaluated the model of the proposed algorithm and the TSK model to prove the superiority.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.2
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• pp.323-329
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• 2001

In this paper, we present a collaborative method for material delivery using a distributed vehicle agents system. Generally used AGV(Autonomous Guided Vehicle) systems in FA require extraordinary facilities like guidepaths and landmarks and have numerous limitations for application in different environments. Moreover in the case of controlling multi vehicles, the necessity for developing corporation abilities like loading and unloading materials between vehicles including different types is increasing nowadays for automation of material flow. Thus to compensate and improve the functions of AGV, it is important to endow vehicles with the intelligence to recognize environments and goods and to determine the goal point to approach. In this study we propose an interaction method between hetero-type vehicles and adaptive fuzzy logic controllers for sensor-based path planning methods and material identifying methods which recognizes color. For the purpose of carrying materials to the goal, simple color sensor is used instead vision system to search for material and recognize its color in order to determine the goal point to transfer it to. The proposed method reaveals a great deal of improvement on its performance.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.5
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• pp.42-49
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• 2001

In This paper, we present a collaborative method for material delivery using a distributed vehicle agents system. Generally used AGV(Autonomous Guided Vehicle) systems in FA(Factory Automation) require extraordinary facilities like guidepaths and landmarks and have numerous limitations for application in different environments. Moreover in the case of controlling multi vehicles, the necessity for developing corporation abilities like loading and unloading materials between vehicles including different types is increasing nowadays for automation of material flow. Thus to compensate and improve the functions of AGV, it is important to endow vehicles with the intelligence to recognize environments and goods and to determine the goal point to approach. In this study we propose an interaction method between hetero-type vehicles and adaptive fuzzy logic controllers for sensor-based path planning methods and material identifying methods which recognizes color. For the purpose of carrying materials to the goal, simple color sensor is used instead of intricate vision system to search for material and recognize its color in order to determine the goal point to transfer it to. The technique for the proposed method will be demonstrated by experiment.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.606-608
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• 1997

In this paper, Neuro-Fuzzy Controller(NFC), a fuzzy system realized using a neural network, is to adopt for the multivariable system. In the multivariable system, the interactive effects between the variables should be taken into account. A simple compensator, using the steady-state information can be obtained for open-loop stable systems, is presented to cope with this problem. However, it should be supposed that the plant is unknown to the control system designer, but an estimate of the DC gain has been obtained by carrying out experiments on the plant. Also, if the variables are not combinated completely, it is difficult to design the controller. Therefore, we design a neuro-fuzzy controller which controls a multivariable system with only input output informations, and compare its performance with that of a PI controller. In the proposed controller, the construction of the membership functions and rule base, which is highly heuristic, can be achieved using a training process. This allows the combination of knowledge of human experts and evidence from input-output data.

• The Journal of Information Technology
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• v.5 no.4
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• pp.73-79
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• 2002

In this paper, I will propose the Niche-Meta Genetic Algorithm that has a multi-mutation operator for design of fuzzy controller. The gene in the proposed algorithm is formed by several parameters that represent the crossover rate, mutation rate and input-output membership functions. The optimization of fuzzy membership function is performed with local search on sub-population and the optimal structure is constructed with global search on total-population. The multi-mutation is selected under basis of the result of local evolution. A simulation for 2 D.O.F wheeled-mobile robot is showed to prove the efficiency of the proposed algorithm

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.5
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• pp.266-274
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• 2004

This paper presents a scheme for the interconnection of dispersed generator systems(DGs) based on load .unbalance and load model in composite distribution systems. Groups of each individual load model consist of residential, industrial, commercial, official and agricultural load. The unbalance is involved with many single-phase line segment. . Voltage profile improvement and system loss minimization by installation of DGs depend greatly on how they are placed and operated in the distribution systems. So, DGs can reduce distribution real power losses and replace large-scale generators if they are placed appropriately in the distribution systems. The main idea of solving fuzzy goal programming is to transform the original objective function and constraints into the equivalent multi-objectives functions with fuzzy sets to evaluate their imprecise nature for the criterion of power loss minimization, the number or total capacity of DGs and the bus voltage deviation, and then solve the problem using genetic algorithm. The method proposed is applied to IEEE 13 bus and 34 bus test systems to demonstrate its effectiveness.