• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.217-220
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• 2001

The fuzzy equalization method does not require the usual learning step for generating fuzzy rules. However it is heavily depend on the given input-output data set. So, we adapt an hierarchical scheme which sequentially optimizes the fuzzy inference system. Here, the parameters of fuzzy membership functions obtained from the fuzzy equalization are optimized by the genetic algorithm, and then they are also modified to increase the performance index using the linguistic hedge. Finally, we applied it to the Rice taste data and got better results than previous ones.

• Journal of information and communication convergence engineering
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• 제10권2호
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• pp.149-155
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• 2012

This paper presents mobile robot control architecture of hierarchical behaviors, inspired by biological life. The system is reactive, highly parallel, and does not rely on representation of the environment. The behaviors of the system are designed hierarchically from the bottom-up with priority given to primitive behaviors to ensure the survivability of the robot and provide robustness to failures in higher-level behaviors. Fuzzy logic is used to perform command fusion on each behavior's output. Simulations of the proposed methodology are shown and discussed. The simulation results indicate that complex tasks can be performed by a combination of a few simple behaviors and a set of fuzzy inference rules.

• 한국항만학회지
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• 제7권1호
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• pp.5-12
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• 1993

In complex decision making such as ill-defined system, one of the main problem is how to treat ambiguous aspect of the decision making. According to the complexity and ambiguity of the objective systems, many types of evaluation attributes are necessary for the rational decision and the relationship among the attributes become complex and fuzzy. Fuzzy integral is very effective to evalute the complex system with interaction between attributes but how to save the evaluation efforts in the decision making process of grading the membership of the objects or alternative is the problem to be tackled. Because the more object there are to evaluate, the number of decisions to made increase exponentially. Therefore, this paper aimes to propose a new evaluation algorithm based on fuzzy integral which can save the evaluator's efforts in decision making process. The proposed algorithm is constructed as follows : First, compose the fuzzy measure by introducing AHP(Analytical Hierachy Process) & mutual interaction coefficient. Second, generate fuzzy measure value of monotone family set for calculating the fuzzy integral. The effectiveness of the proposed algorithm is investigated through the example and sensitivity of interaction coefficient is illustrated.

• Advances in concrete construction
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• 제11권1호
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• pp.1-9
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• 2021

This article discusses the issue of optimizing controller design issues, in which the artificial intelligence (AI) evolutionary bat (EB) optimization algorithm is combined with the fuzzy controller in the practical application of the building. The controller of the system design includes different sub-parts such as system initial condition parameters, EB optimal algorithm, fuzzy controller, stability analysis and sensor actuator. The advantage of the design is that for continuous systems with polytypic uncertainties, the integrated H2/H∞ robust output strategy with modified criterion is derived by asymptotically adjusting design parameters. Numerical verification of the time domain and the frequency domain shows that the novel system design provides precise prediction and control of the structural displacement response, which is necessary for the active control structure in the fuzzy model. Due to genetic algorithm (GA), we use a hierarchical conditions of the Hurwitz matrix test technique and the limits of average performance, Hierarchical Fitness Function Structure (HFFS). The dynamic fuzzy controller proposed in this paper is used to find the optimal control force required for active nonlinear control of building structures. This method has achieved successful results in closed system design from the example.

• 해양환경안전학회지
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• 제2권1호
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• pp.97-106
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• 1996

One of the main problems in evaluating complex objects, such as an ill-defined system, is how to treat ambiguous aspect of the evaluation. Due to the Complexity and ambiguity of the objects, many types of evaluation attributes should be identified based on the rational dsision. One of these attributes is an analytical hierarchy process (AHP). the weight of evaluation attribtes in AHP however comes from the probability measure based on the additivity. Therefore, it is notapplicable to the objects which have the property of non-additivity. In the previous studies by other researchers they intriduced the Hierarchical Fuzzy Integral method or mergd AHP and fuzzy measure for the analysis of the overlaps among the evaluation objects. But, they need more anlyses in terms of transformation of the probability measure into fuzzy measure which fits for the additivity and overlapping coefficient which affects to the fuzzy measure. Considering these matters, this paper deals that, ⅰ) clarifying the relation between the fuzzy and probability measure adopted in AHP, ii) calculating directly the family of fuzzy measure from the overlapping coefficient and probability measure. A simple algorithm for the calculation of fuzzy measures and set family of those from the above results is also proposed. Finally, the effectiveness of the algorithm developed by applying this to the problems for estimation of safety in ship berthing and for evaluation of ports in competition is verified. This implied that the new algoritnm gives better description of the system evaluation.

• KSBB Journal
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• 제16권4호
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• pp.381-388
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• 2001

DNA칩의 유전자 발현 데이터의 통합적 분석을 위하여 매트랩을 기반으로 한 통합분석 프로그램을 구축하였다. 이 프로그램은 유전자 발현 분석을 위해 일반적으로 많이 쓰는 방법인 Hierarchical clustering(HC), K-means, Self-organizing map(SOM), Principal component analysis(PCA)를 지원하며, 이외에 Fuzzy c-means방법과 최근에 발표된 Singular value decomposition(SVD) 분석 방법도 지원하고 있다. 통합분석프로그램의 성능을 알아보기 위하여 효모의 포자형성(sporulation)과 정의 유전자발현 데이터를 사용하였으며, 각 분석 방법에 따른 분석 결과를 제시하였으며, 이 프로그램이 유전자 발현데이타의 통합적인 분석을 위해 효과적으로 사용될 수 있음을 제시하였다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.453-458
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• 1998

In this paper, a new approach to modeling of nonlinear systems using fuzzy theory is presented. So as to handle a variety of nonlinearity and reflect the degree of confidence in the informations about system, we combine multiple model method with hierarchical prioritized structure. The mountain clustering technique is used in partition of system, and TSK rule structure is adopted to form the fuzzy rules. Back propagation algorithm is used for learning parameters in the rules. Computer simulations are performed to verify the effectiveness of the proposed method. It is useful for the treatment fo the nonlinear system of which the quantitative math-approach is difficult.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.120-122
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• 2006

The paper concerns the hybrid optimization of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA) and information data granulation. The granulation is realized with the aid of the Hard C-means clustering and HFCGA is a kind of multi-populations of Parallel Genetic Algorithms (PGA), and it is used for structure optimization and parameter identification of fuzzy model. It concerns the fuzzy model-related parameters such as the number of input variables to be used, a collection of specific subset of input variables, the number of membership functions, the order of polynomial, and the apexes of the membership function. In the hybrid optimization process, two general optimization mechanisms are explored. Thestructural optimization is realized via HFCGA and HCM method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods.

• 전기학회논문지
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• 제67권3호
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• pp.419-426
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• 2018

An useful and effective design method for the gas identification system is presented in this paper. The proposed gas identification system adopts hierarchical structure with two level rule base combining fuzzy sets with rough sets. At first, a hybrid genetic algorithm is used in grouping the array sensors of which the measured patterns are similar in order to reduce the dimensionality of patterns to be analyzed and to make rule construction easy and simple. Next, for low level identification, fuzzy inference systems for each divided group are designed by using TSK fuzzy rule, which allow handling the drift and the uncertainty of sensor data effectively. Finally, rough set theory is applied to derive the identification rules at high level which reflect the identification characteristics of each divided group. Thus, the proposed method is able to accomplish effectively dimensionality reduction as well as accurate gas identification. In simulation, we demonstrated the effectiveness of the proposed methods by identifying five types of gases.

• 전기학회논문지
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• 제58권12호
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• pp.2520-2528
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• 2009

In this paper, we proposed tracking detection methodology using information granulation-based fuzzy radial basis function neural networks (IG-FRBFNN). According to IEC 60112, tracking device is manufactured and utilized for experiment. We consider 12 features that can be used to decide whether tracking phenomenon happened or not. These features are considered by signal processing methods such as filtering, Fast Fourier Transform(FFT) and Wavelet. Such some effective features are used as the inputs of the IG-FRBFNN, the tracking phenomenon is confirmed by using the IG-FRBFNN. The learning of the premise and the consequent part of rules in the IG-FRBFNN is carried out by Fuzzy C-Means (FCM) clustering algorithm and weighted least squares method (WLSE), respectively. Also, Hierarchical Fair Competition-based Parallel Genetic Algorithm (HFC-PGA) is exploited to optimize the IG-FRBFNN. Effective features to be selected and the number of fuzzy rules, the order of polynomial of fuzzy rules, the fuzzification coefficient used in FCM are optimized by the HFC-PGA. Tracking inference engine is implemented by using the LabVIEW and loaded into embedded system. We show the superb performance and feasibility of the tracking detection system through some experiments.