• Journal of Mechanical Science and Technology
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• v.14 no.11
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• pp.1206-1215
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• 2000

This paper introduces the application of evolutionary fuzzy modeling (EFM) in constructing global function approximations to subsequent use in non-gradient based optimizations strategies. The fuzzy logic is employed for express the relationship between input training pattern in form of linguistic fuzzy rules. EFM is used to determine the optimal values of membership function parameters by adapting fuzzy rules available. In the study, genetic algorithms (GA's) treat a set of membership function parameters as design variables and evolve them until the mean square error between defuzzified outputs and actual target values are minimized. We also discuss the enhanced accuracy of function approximations, comparing with traditional response surface methods by using polynomial interpolation and back propagation neural networks in its ability to handle the typical benchmark problems.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.1
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• pp.38-42
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• 2002

A fast iterative solving method of fuzzy relational equation is proposed. It is derived by eliminating a redundant comparison process in the conventional iterative solving method (Pedrycz, 1983). The proposed method is applied to image reconstruction, and confirmed that the computation time is decreased to 1 / 40 with the compression rate of 0.0625. Furthermore, in order to make any initial solution converge on a reconstructed image with a good quality, a new cost function is proposed. Under the condition that the compression rate is 0.0625, it is confirmed that the root mean square error of the proposed method decreases to 27.34% and 86.27% compared with those of the conventional iterative method and a non iterative image reconstruction method, respectively.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.309-312
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• 1999

This paper proposed a novel method for pseudo-on-line scheme using look-up table based on the genetic algorithm The technique is an pseudo-on-line method that optimally estimate the parameters of FPID controller for systems with non-linearity using the genetic algorithm which does not use the gradient and finds the global optimum of an unconstraint optimization problem. The proposed controller is applied to speed control of 3-phase induction motor and its computer simulation is carried out. Simulation results show that the proposed method is more excellent then conventional FPID and PID controllers.

• Journal of the Korea Concrete Institute
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• v.12 no.4
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• pp.91-101
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• 2000

As the computer related technology evolves a study for a practical use of real structure as well as its hteory for optimum design has been greatly advanced. But the study on optimum design of pre-stressed concrete beam(PSC-beam) bridge for the construction of national roads and highways in Korea is not sufficient. Since a standard section for the PSC-beam is proposed, it is practically used in designing the PSC-beam. It is noticed that the section using the current standard PSC-beam design to be an over-designed with its surplus safety factor. Therefore, it is necessary to consider economical PSC-beam section which automatically satisfies all requirement of design specifications. Thus, in this study, the optimum design methods of PSC-beam are carried out using the gradient-based search method and global search method. As a result of the optimum design method, it was confirmed that the design of PSC-beam has a serious properties to non-linearity and discontinuity. And the section that in economical and efficinet design methods than the current standard design method is proposed.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.2
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• pp.85-91
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• 2000

This paper proposes a novel method with pseudo-on-line scheme using the optimized look-up table based on the genetic algorithm which does not use the gradient and finds the global optimum of an un-constraint optimization problem. The technique is a pseudo-on-line method that optimally estimates the parameters of fuzzy PID(FPID) controller for systems with non-linearity, using the genetic algorithm. The proposed controller(GFPID) with the auto-tuning function is applied to the on-line and real-time control of speed at 3-phase induction motor, and its computer simulation is carried out. simulation results show that the proposed methodis more excellent that conventional FPID and PID controllers.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2386-2388
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• 2000

This paper proposes a novel method with pseudo-on-line scheme using look-up table based on the genetic algorithm. The technique is a pseudo-on-line method that optimally estimate the parameters of fuzzy PID(FPID) controller for systems with non-linearity using the genetic algorithm which does not use the gradient and finds the global optimum of an un-constraint optimization problem. The proposed controller(GFPID) is applied to speed control of 3-phase induction motor and its computer simulation is carried out. Simulation results show that the proposed method is more excellent than conventional FPID and PID controllers.

• The Korean Journal of Rheology
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• v.10 no.4
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• pp.202-216
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• 1998

Improved version of previous 'Orthotropic' closure approximation, termed 'ORW' has been numerically developed using new homogeneous flow data. Previous 'Orthotropic' closure approximation, i.e., ORF or ORL showed non-physical oscillation for interaction coefficient $C_1$<0.001 at simple shear flow. It also shows non-physcial oscillation and under-prediction compared with 'Distribution Function Calculation' at non-homogeneous flow of center-gated disk. These phenomena are mainly due to the flow data of 'Distribution Function Calculation' which were used for least-square optimization. ORW obtained by fitting flow data of low interaction coefficient does not show non-physical oscillation and results in reasonably good behaviors at non-homogeneous flows as well as homogeneous flows. Fitting function forms have not been found to improve overall behaviors. It has been found that considering all the eigenvalues of orientation tensor (including the third eigenvalues) might end up with a better closure approximation than just considering the first and second eigenvalues. It is, however, very important and yet difficult to select appropriate function forms of eigenvalues. Numerical simulation including coupling and in-plane velocity gradient effects were performed for injection mold filing process with a film-gated strip and a center-gated disk using ORW and various other closure approximations for comparisons. Although ORW is in excellent agreement with 'Distribution Function Calculation', the predicted results seem to have consistent error in comparison with experimental data. The diffusivity term with constant interaction coefficient might have to be further investigated in order to accurately describe orientation states.

• Computers and Concrete
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• v.2 no.5
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• pp.345-366
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• 2005

This work presents an assessment of the computational performance of a vector-parallel implementation of probabilistic model for concrete cracking in 3D. This paper shows the continuing efforts towards code optimization as reported in earlier works Paz, et al. (2002a,b and 2003). The probabilistic crack approach is based on the direct Monte Carlo method. Cracking is accounted by means of 3D interface elements. This approach considers that all nonlinearities are restricted to interface elements modeling cracks. The heterogeneity governs the overall cracking behavior and related size effects on concrete fracture. Computational kernels in the implementation are the inexact Newton iterative driver to solve the non-linear problem and a preconditioned conjugate gradient (PCG) driver to solve linearized equations, using an element by element (EBE) strategy to compute matrix-vector products. In particular the paper analyzes code behavior using OpenMP directives in parallel vector processors (PVP), such as the CRAY SV1 and CRAY T94. The impact of the memory architecture on code performance, and also some strategies devised to circumvent this issue are addressed by numerical experiment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.4
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• pp.554-561
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• 2013

In this paper, we introduce a modeling and a process simulator developed with the aid of pRBFNNs for activated sludge process in the sewage treatment system. Activated sludge process(ASP) of sewage treatment system facilities is a process that handles biological treatment reaction and is a very complex system with non-linear characteristics. In this paper, we carry out modeling by using essential ASP factors such as water effluent quality, the manipulated value of various pumps, and water inflow quality, and so on. Intelligent algorithms used for constructing process simulator are developed by considering multi-output polynomial radial basis function Neural Networks(pRBFNNs) as well as Fuzzy C-Means clustering and Particle Swarm Optimization. Here, the apexes of the antecedent gaussian functions of fuzzy rules are decided by C-means clustering algorithm and the apexes of the consequent part of fuzzy rules are learned by using back-propagation based on gradient decent method. Also, the parameters related to the fuzzy model are optimized by means of particle swarm optimization. The coefficients of the consequent polynomial of fuzzy rules and performance index are considered by the Least Square Estimation and Mean Squared Error. The descriptions of developed process simulator architecture and ensuing operation method are handled.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.1
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• pp.26-32
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• 2005

In this paper, the effect of various regression models is investigated on structural optimization using the central composite method. Three bar truss and the upper platform of a satellite are optimized using various regression models that are polynomial, exponential and log functions. Response surface method is non-gradient, semi-global, discrete and fast converging in optimization problem. Sampling points are extracted by the design of experiments using the central composite method. Response surface is generated using the various regression functions. Structural analysis for calculating constraints is executed to find static and dynamic responses. From this study, it is verified that the response surface method has advantage in optimum value and computation time in comparison to other optimization methods.