• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.421-426
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• 2005

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes particle swarm optimization algorithm based optimal learning fuzzy-neural network (PSOA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by particle swarm optimization algorithm. The learning algorithm of the PSOA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, particle swarm optimization algorithm is used for tuning of membership functions of the proposed model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.6B
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• pp.645-651
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• 2002

This thesis describes two wavelength allocation algorithms applied to OMS (Optical Multiplex Section) or OCh (Optical Channel) SPRING (Shared Protection Ring) and compares their characteristics by simulations. Two wavelength allocation algorithms are optimal algorithm and nonblocking algorithm applicable on SPRING WDM networks. In particular, when a node is added in previous ring network, how to work for each algorithm is considered. The optimal algorithm is better than nonblocking for most of comparisons. Nonblocking algorithm has an important advantage, though. Nonblocking algorithm has no wavelength connection or allocation to reconfigure some previous connections.

• Journal of the Korea Society of Computer and Information
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• v.20 no.9
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• pp.81-86
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• 2015

This paper suggests heuristic algorithm with linear time complexity to decide the normal and optimal point at minimum loss/maximum profit maximum shortest scheduling problem with additional loss cost and bonus profit cost. This algorithm computes only the earliest ending time for each node. Therefore, this algorithm can be get the critical path and project duration within O(n) time complexity and reduces the five steps of critical path method to one step. The proposed algorithm can be show the result more visually than linear programming and critical path method. For real experimental data, the proposed algorithm obtains the same solution as linear programming more quickly.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.936-943
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• 2002

The Simulated Annealing (SA) algorithm has been successfully applied to various difficult combinatorial optimization problems. Since the performance of a SA algorithm, generally depends on values of the parameters, it is important to select the most appropriate parameter values. In this paper the SA algorithm is optimally designed for performance acceleration, by using the Taguchi method. Several test problems are solved via the SA algorithm optimally designed, and the solutions obtained are compared to solution results McMullen & Frazier(2000). The performance of the SA algorithm is evaluated in terms of solution quality and computation times. Computational results show that the proposed SA algorithm is effective and efficient in finding near-optimal solutions.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.654-656
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• 1998

This paper presents an optimal identification method of nonlinear and complex system that is based on fuzzy-neural network(FNN). The FNN used simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM Algorithm to find initial parameters of membership function. And then to obtain optimal parameters, we use the genetic algorithm. Genetic algorithm is a random search algorithm which can find the global optimum without converging to local optimum. The parameters such as membership functions, learning rates and momentum coefficients are easily adjusted using the genetic algorithms. Also, the performance index with weighted value is introduced to achieve a meaningful balance between approximation and generalization abilities of the model. To evaluate the performance of the FNN, we use the time series data for 9as furnace and the sewage treatment process.

• Journal of the Korea Safety Management & Science
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• v.13 no.3
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• pp.107-114
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• 2011

Traveling salesman problem is to minimize the total cost for a traveling salesman who wants to make a tour given finite number of cities along with the cost of travel between each pair them, visiting each cities exactly once before returning home. Traveling salesman problem is known to be NP-hard, and it needs a lot of computing time to get the optimal solution, so that heuristics are more frequently developed than optimal algorithms. This study suggests a hybrid parallel genetic algorithm(HPGA) for traveling salesman problem The suggested algorithm combines parallel genetic algorithm, nearest neighbor search, and 2-opt. The suggested algorithm has been tested on 7 problems in TSPLIB and compared the results of existing methods(heuristics, meta-heuristics, hybrid, and parallel). Experimental results shows that HPGA could obtain good solution in total travel distance minimization.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.10
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• pp.149-156
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• 2010

In this paper, the optimal design of a wind generator, implemented with the hybridized GA(Genetic Algorithm) and ES(Expert System), has been performed to maximize the AEP(Annual Energy Production) over the whole wind speed characterized by the statistical model of wind speed distribution. In particular, to solve the problem of calculation iterate, ES finds the superior individual and apply to initial generation of GA and it makes reduction of search domain. Meanwhile, for effective searching in reduced search domain, it propose Intelligent GA algorithm. Also, it shows the results of optimized model 500[kW] wind generator using hybridized algorithm and benchmark result of compare with GA.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.674-682
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• 2017

This paper presents an optimal initial firing angle control based on the energy consumption and regenerative energy of a parallel multi-pulse thyristor dual converter for urban railway power substations. To prevent short circuiting the thyristor dual converter, a hysteresis band for maintaining a zero-current discontinuous section (ZCDS) is essential during mode changes. During conversion from the ZCDS to forward or reverse mode, the DC trolley voltage can be stabilized by selecting the optimal initial firing angle without an overshoot and slow response. However, the optimal initial firing angle is different depending on the line impedance of each converter. Therefore, the control algorithm for tracking the optimal initial firing angle is proposed to eliminate the overshoot and slow response of DC trolley voltage. Simulations and experiments show that the proposed algorithm yields the fastest DC voltage control performance in the transient state by tracking the optimal firing angle.

• Journal of Mechanical Science and Technology
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• v.20 no.12
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• pp.2115-2123
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• 2006

The paper deals with the identification of Pareto optimal solutions using GA based coevolution in the context of multiobjective optimization. Coevolution is a genetic process by which several species work with different types of individuals in parallel. The concept of cooperative coevolution is adopted to compensate for each of single objective optimal solutions during genetic evolution. The present study explores the GA based coevolution, and develops prescribed and adaptive scheduling schemes to reflect design characteristics among single objective optimization. In the paper, non-dominated Pareto optimal solutions are obtained by controlling scheduling schemes and comparing each of single objective optimal solutions. The proposed strategies are subsequently applied to a three-bar planar truss design and an energy preserving flywheel design to support proposed strategies.

• Journal of the korean Society of Automotive Engineers
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• v.15 no.3
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• pp.36-46
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• 1993

In CVT vehicle, the engine speed is completely decoupled from the vehicle speed within the range from maximum transmission ratio to minimum transmission ratio. This allows the engine to operate in optimal state(e.g. best fuel economy or maximum power mode.) In this study, the CVT control algorithm for optimal operation of engine is suggested. In order to implement the real time digital control of electro-hydraulic CVT system, a software called CVTCON has been developed. CVTCON also includes the CVT operation module, (2) system test module, (3) system control module and (4) data management module. By using the CVTCON and the electro-hydraulic CVT system, two modes of experiments were carried out: constant throttle opening mode and acceleration mode. From the experimental result, it was found that the algorithm suggested in this study showed optimal operation of the CVT system.