• 한국전산구조공학회:학술대회논문집
• /
• 한국전산구조공학회 2002년도 봄 학술발표회 논문집
• /
• pp.274-283
• /
• 2002

This paper presents an efficient models for reinforeced concrete structures using CART-ANFIS(classification and regression tree-adaptive neuro fuzzy inference system). a fuzzy decision tree parttitions the input space of a data set into mutually exclusive regions, each of which is assigned a label, a value, or an action to characterize its data points. Fuzzy decision trees used for classification problems are often called fuzzy classification trees, and each terminal node contains a label that indicates the predicted class of a given feature vector. In the same vein, decision trees used for regression problems are often called fuzzy regression trees, and the terminal node labels may be constants or equations that specify the Predicted output value of a given input vector. Note that CART can select relevant inputs and do tree partitioning of the input space, while ANFIS refines the regression and makes it everywhere continuous and smooth. Thus it can be seen that CART and ANFIS are complementary and their combination constitutes a solid approach to fuzzy modeling.

• 한국지능시스템학회논문지
• /
• 제6권4호
• /
• pp.61-70
• /
• 1996

이 논문은 인간의 주관적인 판단에 의한 의식구조문제를 취급한다. 의식구조를모델링하는 방법으로 퍼지구조 모델링법이 있으나, 이 방법은 인간의 주관적인 판단의 일대비교의 회수가 많고, 판단에 대한 전도라고 할 수 있는 정합성을 체크하기 어렵다. 이러한 점들을 개선하기 위한 방법으로서 AHP에서의 일대비교행렬의 개념을 이용한 행렬에 의한 퍼지구조모델링법으로서 의식을 구조화한다. 이 방법의 유효성은 품질시스템구축에 대한 의식 구조그래프에 의하여 보이고자 한다.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제5권4호
• /
• pp.327-332
• /
• 2005

We investigatea new fuzzy-neural networks-Hybrid Fuzzy set based polynomial Neural Networks (HFSPNN). These networks consist of genetically optimized multi-layer with two kinds of heterogeneous neurons thatare fuzzy set based polynomial neurons (FSPNs) and polynomial neurons (PNs). We have developed a comprehensive design methodology to determine the optimal structure of networks dynamically. The augmented genetically optimized HFSPNN (namely gHFSPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional HFPNN. The GA-based design procedure being applied at each layer of gHFSPNN leads to the selection leads to the selection of preferred nodes (FSPNs or PNs) available within the HFSPNN. In the sequel, the structural optimization is realized via GAs, whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gHFSPNN is quantified through experimentation where we use a number of modeling benchmarks synthetic and experimental data already experimented with in fuzzy or neurofuzzy modeling.

• 대한기계학회논문집A
• /
• 제25권3호
• /
• pp.537-547
• /
• 2001

The paper proposes the fuzzy logic global approximate optimization strategies in optimal sizing of automotive A-pillar trim with rib structures for occupant head protection. Two different strategies referred to as evolutionary fuzzy modeling (EFM) and neuro-fuzzy modeling (NFM) are implemented in the context of global approximate optimization. EFM and NFM are based on soft computing paradigms utilizing fuzzy systems, neural networks and evolutionary computing techniques. Such approximation methods may have their promising characteristics in a case where the inherent nonlinearity in analysis model should be accommodated over the entire design space and the training data is not sufficiently provided. The objective of structural design is to determine the dimensions of rib in A-pillar, minimizing the equivalent head injury criterion HIC(d). The paper describes the head-form modeling and head impact simulation using LS-DYNA3D, and the approximation procedures including fuzzy rule generation, membership function selection and inference process for EFM and NFM, and subsequently presents their generalization capabilities in terms of number of fuzzy rules and training data.

• 한국지능시스템학회논문지
• /
• 제25권1호
• /
• pp.85-90
• /
• 2015

본 논문은 중소기업의 CEO의 핵심역량 24개를 FSM을 이용하여 구조분석을 하고 5개의 그룹으로 분류하였다. 또한 CEO의 업무별로 CEO의 업무능력과 핵심역량과의 관련성을 파악하기 위해 회귀분석을 실시하였다. 본 논문의 특징은 중소기업 CEO의 역량에 대한 분류와 구조화를 통한 층별 상호간의 관계를 알 수 있고, CEO의 업무능력에 무슨 역량그룹이 영향을 주는지를 알 수 있게 해준다.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
• /
• pp.155-157
• /
• 2005

We propose a new category of hybrid multi-layer neural networks with hetero nodes such as Fuzzy Set based Polynomial Neurons (FSPNs) and Polynomial Neurons (PNs). These networks are based on a genetically optimized multi-layer perceptron. We develop a comprehensive design methodology involving mechanisms of genetic optimization and genetic algorithms, in particular. The augmented genetically optimized HFPNN (namely gHFPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional HFPNN. The GA-based design procedure being applied at each layer of HFPNN leads to the selection of preferred nodes (FPNs or PNs) available within the HFPNN. In the sequel, two general optimization mechanisms are explored. First, the structural optimization is realized via GAs whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gHFPNNs quantified through experimentation where we use a number of modeling benchmarks-synthetic and experimental data already experimented with in fuzzy or neurofuzzy modeling.

• Geomechanics and Engineering
• /
• 제36권5호
• /
• pp.475-487
• /
• 2024

Many uncertainties affect the stability assessment of rock structures. Some of these factors significantly influence technology decisions. Some of these factors belong to the geological domain, and spatial uncertainty measurements are useful for structural stability analysis. This paper presents an integrated approach to study the stability of rock structures, including spatial factors. This study models two main components: discrete structures (fault zones) and well known geotechnical parameters (rock quality indicators). The geostatistical modeling criterion are used to quantify geographic uncertainty by producing simulated maps and RQD values for multiple equally likely error regions. Slope stability theorem would be demonstrated by modeling local failure zones and RQDs. The approach proided is validated and finally, the slope stability analysis method and fuzzy Laypunov criterion are applied to mining projects with limited measurement data. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Simulation results of linear and nonlinear structures show that the proposed method is able to identify structural parameters and their changes due to damage and unknown excitations. Therefore, the goal is believed to achieved in the near future by the ongoing development of AI and fuzzy theory.

• Structural Engineering and Mechanics
• /
• 제61권2호
• /
• pp.283-293
• /
• 2017

The main contribution of the present paper is to propose an intelligent fuzzy inference system approach for modeling the debonding strength of masonry elements retrofitted with Fiber Reinforced Polymer (FRP). To achieve this, the hybrid of meta-heuristic optimization methods and adaptive-network-based fuzzy inference system (ANFIS) is implemented. In this study, particle swarm optimization with passive congregation (PSOPC) and real coded genetic algorithm (RCGA) are used to determine the best parameters of ANFIS from which better bond strength models in terms of modeling accuracy can be generated. To evaluate the accuracy of the proposed PSOPC-ANFIS and RCGA-ANFIS approaches, the numerical results are compared based on a database from laboratory testing results of 109 sub-assemblages. The statistical evaluation results demonstrate that PSOPC-ANFIS in comparison with ANFIS-RCGA considerably enhances the accuracy of the ANFIS approach. Furthermore, the comparison between the proposed approaches and other soft computing methods indicate that the approaches can effectively predict the debonding strength and that their modeling results outperform those based on the other methods.

• 한국지능시스템학회논문지
• /
• 제21권3호
• /
• pp.378-382
• /
• 2011

본 논문에서는 제조업에 종사하는 현장근로자의 핵심역량을 개발하고 이 핵심역량에 대한 의식구조를 분석한다. 의식구조의 분석방법으로 일반적으로는 ISM과 FSM을 각각 사용하여 층을 분류하고 그 연결 상태를 파악하게 된다. 그러나, 데이터에 따라 각 층의 요인들이 달라지는 경우가 많이 발생하게 되는데 이것은 기본적으로 구조는 정해져있고 그 연결고리가 방법에 따라 달라질 수 있다는 관점에서 본 논문에서는 ISM을 통하여 먼저 구조모델을 결정하고, 연결고리는 FSM으로 결정하는 방법을 제시하고자 하였다. 이 방법을 이용하여 제조업의 현장관리자의 핵심역량에 대한 의식구조를 분석하는데 전문가의 확인을 통해 보다 객관성 있는 구조모델을 제시하였다.

• 한국지능시스템학회논문지
• /
• 제25권6호
• /
• pp.607-614
• /
• 2015

본 논문은 항공기 정비사의 핵심역량 16개에 대하여 정비능력향상을 목적으로 FSM을 이용하여 구조분석을 실시하여 최상층 3개, 중간층 3개, 하위층 10개로 분류되는 계층별 구조를 핵심역량간의 연결상태와 중요도를 파악하였다. 또한 항공기 정비사의 핵심역량은 업무를 통해서 정비품질 및 생산성이 향상될 수 있다는 관점에서 핵심역량과 업무를 퍼지관계를 이용하여 100명의 항공기 정비사의 설문을 통하여 퍼지관계행렬을 구하여 업무를 평가하는 기준으로 사용하고자 하였다. 본 논문에서는 모델링데이터 100개와 체킹데이터 67개를 사용하여 모델의 유효성을 보였다.