• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.487-496
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• 2000

In this paper, the Multi-FNN(Fuzzy-Neural Networks) model is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNN is based on Yamakawa's FNN and uses simplified inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and Genetic Algorithms(GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. The aggregate performance index stands for an aggregate objective function with a weighting factor to consider a mutual balance and dependency between approximation and predictive abilities. According to the selection and adjustment of a weighting factor of this aggregate abjective function which depends on the number of data and a certain degree of nonlinearity, we show that it is available and effective to design an optimal Multi-FNN model. To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.4
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• pp.343-350
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• 2000

In this paper, we propose the Multi-FPNN(Fuzzy Polynomial Neural Networks) model based on FNN and PNN(Polyomial Neural Networks) for optimal system identifacation. Here FNN structure is designed using fuzzy input space divided by each separated input variable, and urilized both in order to get better output performace. Each node of PNN structure based on GMDH(Group Method of Data handing) method uses two types of high-order polynomials such as linearane and quadratic, and the input of that node uses three kinds of multi-variable inputs such as linear and quadratic, and the input of that node and Genetic Algorithms(GAs) to identify both the structure and the prepocessing of parameters of a Multi-FPNN model. Here, HCM clustering method, which is carried out for data preproessing of process system, is utilized to determine the structure method, which is carried out for data preprocessing of process system, is utilized to determance index with a weighting factor is used to according to the divisions of input-output space. A aggregate performance inddex with a wegihting factor is used to achieve a sound balance between approximation and generalization abilities of the model. According to the selection and adjustment of a weighting factor of this aggregate abjective function which it is acailable and effective to design to design and optimal Multi-FPNN model. The study is illustrated with the aid of two representative numerical examples and the aggregate performance index related to the approximation and generalization abilities of the model is evaluated and discussed.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.6
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• pp.635-641
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• 2010

Time-varying channel characteristics in OFDM systems over doubly selective channels cause inter-carrier interferences(ICI) in the frequency domain. Time domain windowing gives rise to restriction on the bandwidth of the frequency domain channel matrix and makes it possible to approximate the OFDM system as a simplified linear input-output model. When successive interference cancellation based on linear MMSE estimation is employed for channel equalization in OFDM systems, symbol detection ordering produces considerable effects on overall system performances. In this paper, we show the reduction of the residual ICI by time domain windowing and the resultant performance improvements, and investigate the effects of SINR- and CSEP-based symbol detection ordering on the performance of successive interference cancellation.

• Journal of Korea Game Society
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• v.18 no.5
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• pp.123-132
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• 2018

Various methods have been proposed to efficiently animate the motion of soft objects in realtime. In order to maintain the topology between the elements of the objects, it is required to employ constraint forces, which limit the size of the time steps for the numerical integration and reduce the efficiency. To tackle this, an implicit method with larger steps was proposed. However, the method is, in essence, a linear system with a large matrix, of which solution requires heavy computations. Several approximate methods have been proposed, but the approximation is obtained with an increased damping and the loss of accuracy. In this paper, new integration method based on harmonic oscillation with better stability was proposed, and it was further stabilized with the hybridization with approximate implicit method. GPU parallelism can be easily implemented for the method, and large-scale soft objects can be simulated in realtime.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.4
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• pp.329-334
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• 2017

The structures, which are made up with the huge number of degrees-of-freedom and the assembly of substructures, have a great complexity. In order to increase the computational efficiency, the analysis models have to be simplified. Many substructuring techniques have been developed to simplify large-scale engineering problems. The techniques are very powerful for solving nonlinear problems which require many iterative calculations. In this paper, a modal derivatives-based model order reduction method, which is able to capture the stretching-bending coupling behavior in geometrically nonlinear systems, is adopted and investigated for its performance evaluation. The quadratic terms in nonlinear beam theory, such as Green-Lagrange strains, can be explained by the modal derivatives. They can be obtained by taking the modal directional derivatives of eigenmodes and form the second order terms of modal reduction basis. The method proposed is then applied to a co-rotational finite element formulation that is well-suited for geometrically nonlinear problems. Numerical results reveal that the end-shortening effect is very important, in which a conventional modal reduction method does not work unless the full model is used. It is demonstrated that the modal derivative approach yields the best compromised result and is very promising for substructuring large-scale geometrically nonlinear problems.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.4
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• pp.359-365
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• 2001

In this paper, we propose the design methodology of target tracking system using fuzzy basis function expansion(FBFE) based on virus evolutionary genetic algorithm (VEGA). In general, the objective of target tracking is to estimate the future trajectory of the target based on the past position of the target obtained from the sensor. In the conventional and mathematical nonlinear filtering method such as extended Kalman filter(EKF), the performance of the system may be deteriorated in highly nonlinear situation. To resolve these problems of nonlinear filtering technique, by appling artificial intelligent technique to the tracking control of moving targets, we combine the advantages of both traditional and intelligent control technique. In the proposed method, after composing training datum from the parameters of extended Kalman filter, by combining FDFE, which has the strong ability for the approximation, with VEGA, which prevent GA from converging prematurely in the case of lack of genetic diversity of population, and by idenLifying the parameters and rule numbers of fuzzy basis function simultaneously, we can reduce the tracking error of EKF. Finally, the proposed method is applied to three dimensional tracking problem, and the simulation results shows that the tracking performance is improved by the proposed method.

• Journal of Korean Society of Steel Construction
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• v.16 no.1 s.68
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• pp.123-134
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• 2004

In this research, a multilevel decomposition technique to enhance the efficiency of the configuration optimization of truss structures was proposed. On the first level, the nonlinear programming problem was formulated considering cross-sectional areas as design variables, weight, or volume as objective function and behavior under multiloading condition as design constraint. Said nonlinear programming problem was transformed into a sequential linear programming problem. which was effective in calculation through the approximation of member forces using behavior space approach. Such approach has proven to be efficient in sensitivity analysis and different form existing shape optimization studies. The modified method of feasible direction (MMFD) was used for the optimization process. On the second level, by treating only shape design variables, the optimum problem was transformed into and unconstrained optimal design problem. A unidirectional search technique was used. As numerical examples, some truss structures were applied to illustrate the applicability. and validity of the formulated algorithm.

• Journal of Korean Association for Spatial Structures
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• v.12 no.1
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• pp.77-85
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• 2012

The membrane structure maintains stable form by giving initial tension to ductile membrane and increasing the stiffness of exterior that is much adopted in the large span spatial structure by making its thickness thin. This kind of membrane structure has characteristic that can express free-form curve, so the selection of structural form is very important. So, this paper proposes the expression of free-form surface based on NURBS basis function and the finite element method considering geometrical nonlinearity for the deduction of large deformation result. Also, for minimizing the approximation of the surface that is derived from the form-finding result, the interface method that change finite element mesh to NURBS is proposed. So, the optimum surface of free-form membrane is derived.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.428-432
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• 2007

댐 제방 등의 붕괴로 인하여 발생하는 급격한 유량의 변화와 흐름영역의 변화로 인한 천이류 및 도수의 발생, 불규칙한 하천단면에서 갈수기 저수기의 흐름해석은 기존의 수치해법의 한계로 인하여 수리모형실험 및 경험식 또는 단면의 단순화 등에 의존하고 있는 실정이다. 본 연구에서는 자연하천에서 비선형 흐름율 계산에 불연속초기조건의 해석해인 Riemann 근사해법을 사용하여 수치적으로 안정되고 정확한 1차원 모형을 개발하고자 한다. 이를 위하여 유한체적법을 사용하였고, 수위와 유량의 계산을 위하여 요구되는 유한체적을 유출입하는 흐름율의 계산에 HLL Riemann 해법을 사용하였으며, MUSCL 기법으로 2차 정확도기법으로 확장하였다. Riemann 해법을 통하여 계산된 비선형의 흐름율과 보존 특성을 만족시켜줄 수 있는 하상 및 하폭변화로 인한 생성항을 처리하는 기법을 제안함으로서 새로운 1차원 수치해석모형을 개발하였다. 개발된 모형의 실제하천의 적용성을 확인하기 위하여 하상과 하폭이 변화하는 부정류 흐름에 적용하여 모형의 적용성 및 정확성을 검증하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.138-143
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• 2006

This paper presents intelligent digital redesign method for hybrid state space fuzzy-model-based controllers. For effectiveness and stabilization of continuous-time uncertain nonlinear systems under discrete-time controller, Takagi-Sugeno(TS) fuzzy model is used to represent the complex system. And global approach design problems viewed as a convex optimization problem that we minimize the error of the norm bounds between nonlinearly interpolated linear operators to be matched. Also, by using the bilinear and inverse bilinear approximation method, we analyzed nonlinear system's uncertain parts more precisely. When a sampling period is sufficiently small, the conversion of a continuous-time structured uncertain nonlinear system to an equivalent discrete-time system have proper reason. Sufficiently conditions for the global state-matching of the digitally controlled system are formulated in terms of linear matrix inequalities (LMIs). Finally, a TS fuzzy model for the chaotic Lorentz system is used as an . example to guarantee the stability and effectiveness of the proposed method.