• Computers and Concrete
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• 제32권1호
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• pp.27-44
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• 2023

The current study targets to apply the adaptive neuro-fuzzy inference system (ANFIS) for the estimation of the shear resistance offered by the externally bonded fiber-reinforced polymer (EB-FRP) U-jackets. A total of 202 groups of data cumulated from previous investigations, were employed for the development and evaluation of the ANFIS model. A relative appraisal between the ANFIS predictions and the results of experiments has shown that the assessments by current ANFIS model are in good concurrence with the latter. In addition, assessment of the accuracy of the ANFIS model was done by relating the ANFIS predictions with the forecasts of eight extensively used design guidelines. Based on the examination of various performance measures, it has been derived that the adequacy of the ANFIS model is better than the available guidelines. A parametric investigation has additionally been done to reconnoiter the influence of individual parameters as well as their combined effects on the shear contribution of EB-FRP. Based on the observations made from the parametric study, it has been witnessed that the ANFIS model has incorporated the effect of different parameters more competently than the considered design guidelines.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2019년도 추계학술대회
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• pp.174-175
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• 2019

Battery cells are connected in parallel to enlarge the system capacity. However, cell inconsistency may reduce the overall system capacity and cause the over-charging or over-discharging issue. This paper proposes a SOC-based sequencing equalizer for parallel-connected battery configuration that uses the ANFIS (adaptive neuro-fuzzy inference system) algorithm to make the switching decision. Depend on the load current and the SOC (state-of-charge) rate of cells, the switching decision is made to equalize the SOC of the battery cells. The simulation results show that the system capacity is maximized and the controller is adaptive for a large number of parallel-connected in dynamic load profile.

• 제어로봇시스템학회논문지
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• 제8권7호
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• pp.589-598
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• 2002

The color measuring/matching expert system is implemented with a new color measuring method that combines intelligent algorithms with image processing techniques. Color measuring part of the proposed system preprocesses the scanned original color input images to eliminate their distorted components by means of the image histogram technique of image pixels, and then extracts RGB(Red, Green, Blue)data among color information from preprocessed color input images. If the extracted RGB color data does not exist on the matching recipe databases, we can measure the colors for the user who want to implement the model that can search the rules for the color mixing information, using the intelligent modeling techniques such as fuzzy inference system and adaptive neuro-fuzzy inference system. Color matching part can easily choose images close to the original color for the user by comparing information of preprocessed color real input images with data-based measuring recipe information of the expert, from the viewpoint of the delta Eformula used in practical process.

• Journal of Power Electronics
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• 제16권2호
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• pp.564-571
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• 2016

This study proposes an adaptive neuro-fuzzy inference system (ANFIS)-based rotor position controller for brushless direct current (BLDC) motors to improve the control performance of the drive under transient and steady-state conditions. The dynamic response of a BLDC motor to the proposed ANFIS controller is considered as standard reference input. The effectiveness of the proposed controller is compared with that of the proportional integral derivative (PID) controller and fuzzy PID controller. The proposed controller solves the problem of nonlinearities and uncertainties caused by the reference input changes of BLDC motors and guarantees a fast and accurate dynamic response with an outstanding steady-state performance. Furthermore, the ANFIS controller provides low torque ripples and high starting torque. The detailed study includes a MATLAB-based simulation and an experimental prototype to illustrate the feasibility of the proposed topology.

• 대한토목학회논문집
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• 제31권2D호
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• pp.217-225
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• 2011

현재 우리나라는 자동차 보유대수가 지속적으로 증가함에 따라 교통 혼잡 문제와 환경 문제가 심각한 실정이다. 환경적으로 지속가능한 교통과 녹색 교통수단에 대한 관심이 증대되면서 최근 정부는 자전거이용 활성화 정책을 추진하고 있다. 이에 맞추어 자전거 이용자들이 느끼는 서비스수준을 분석할 수 있는 모형의 개발이 요망된다. 본 연구에서는 자전거도로 이용자들에 영향을 미치는 인자들 중 퍼지적 성격을 지닌 자전거도로 폭, 대면횟수, 보행자 교통량을 선택하여 자전거도로 서비스수준 분석을 위한 ANFIS 모형을 구축하였다. 이렇게 구축된 모형의 예측력은 실측치와 추론치를 비교함으로써 평가하였다. 결정계수 $R^2$와 오차 및 분산정도를 나타내는 척도인 평균절대오차(MAE)와 평균제곱근오차(MSE)가 각각 0.987, 0.142, 0.032로 나타났으며, 모형의 설명력이 높은 것으로 평가된다. 본 연구에서의 자전거도로 서비스수준이 KHCM에 의한 평가치보다 1~3단계 낮게 나타났다. 이는 본 연구에서 추정 된 서비스수준이 보행자 교통량 이외에 자전거도로 폭과 대면횟수를 고려한 이용자가 느끼는 만족도에 기초하여 서비스수준을 도출하였기 때문으로 판단된다.

• 한국멀티미디어학회:학술대회논문집
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• 한국멀티미디어학회 2002년도 추계학술발표논문집
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• pp.129-132
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• 2002

본 논문은 웨이브렛 변환 다중해상도 분해(multi-resolution Analysis : MRA)와 적응성 뉴로-퍼지 인터페이스 시스템(Adaptive Neuro-Fuzzy Inference System : ANFIS)을 기반으로 한 웨이브렛 신경망을 가지고 임의의 비선형 함수 학습 근사화를 개선하는 것이다. ANFIS 구조는 벨형 퍼지 함수로 구성이 되었고, 웨이브렛 신경망은 전파 알고리즘과 역전파 신경망 알고리즘으로 구성되었다. 여기 웨이브렛 구성은 단일 크기이고, ANFIS 기반 웨이브렛 신경망의 학습을 위해 역전파 알고리즘을 사용하였다. 1차원과 2차원 함수에서 웨이브렛 전달 파라미터 학습과 ANFIS의 벨형 소속 함수를 이용한 ANFIS 모델 기반 웨이브렛 신경망의 웨이브렛 기저 수 감소와 수렴 속도 성능이 기존의 알고리즘 보다 개선되었음을 확인하였다.

• 대한토목학회논문집
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• 제31권5B호
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• pp.439-447
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• 2011

변화할 것으로 예측되고 있는 기후환경에서 현재 수공구조물의 적응능력을 평가하고 지속가능한 시스템을 만들고자 하는 것은 최근의 수자원 관리의 핵심이다. 본 연구에서는 한강수계 5개의 댐을 대상으로 다양한 유입량에 따른 방류량 및 저류량의 변화를 퍼지 추론 시스템을 이용하여 분석하였다. 유입량의 변화에 대한 최대 저류량 및 최소 저류량의 변화를 저수지의 적응능력이라 정의하여 분석한 결과, 저류용량이 상대적으로 작은 광동댐은 유입량의 급격한 증가를 감당하기 어려우며, 소양강댐은 강우량 변화에 대한 적응능력이 가장 뛰어난 것으로 판단되었다. 그러나 퍼지 추론 시스템은 소속함수를 임의로 지정하고, 과거 자료를 이용하여 검증하기가 용이하지 않으므로, 보다 정확하고 효율적인 모의를 위해 소양강댐을 대상으로 적응 신경망-퍼지추론 시스템을 구축하여 적응능력을 평가하였다. 과거 자료의 빈도분석 결과와 기후변화 시나리오를 바탕으로 구축된 9개의 강우 시나리오에 대해 소양강댐의 방류량 및 저류량을 모의한 결과, 유입 시나리오에 따라 매우 상이한 저수지 운영결과를 나타냄을 알 수 있으며, 적응 신경망-퍼지 추론 시스템이 변화하는 강우량과 패턴에도 불구하고 안정적으로 저수지를 운영함을 알 수 있었다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2261-2263
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• 2001

Fuzzy controller design consists of intuition, and any other information about how to control system, into a set of rules. These rules can then be applied to the system. It is very important to decide parameters of IF-THEN rules. Because fuzzy controller can make more adequate force to the plant by means of parameter optimization, which is accomplished by learning procedure. In this paper, we apply fuzzy controller designed to the Swing-UP Inverted pendulum.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.1
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• pp.115-120
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• 2006

This article propose a new idea of developing a hybrid scheme to achieve faster INS alignment with higher accuracy using a novel procedure to estimate the initial attitude angles that combines a Kalman filter and Adaptive Neuro-Fuzzy Inference System architecture. A tactical grade inertial measurement unit was applied to verify the performance of proposed scheme in this study. The preliminary results indicated the outstanding improvements in both time consumption for fine alignment process and accuracy of estimated attitude angles, especially in heading angles. In general, the improvement in terms of time consumption and the accuracy of estimated attitude estimated accuracy reached 80% and 70% respectively during alignment process after compensating the attitude angles estimated by an extended Kalman filter with 15 states using proposed approach. It is worth mentioned that the proposed approach can be implemented in general real time navigation applications.

• Structural Engineering and Mechanics
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• 제47권3호
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• pp.401-419
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• 2013

In this study, the efficiency of adaptive neuro-fuzzy inference system (ANFIS) and genetic expression programming (GEP) in predicting the effects of infill walls on base reactions and roof drift of reinforced concrete frames were investigated. Current standards generally consider weight and fundamental period of structures in predicting base reactions and roof drift of structures by neglecting numbers of floors, bays, shear walls and infilled bays. Number of stories, number of bays in x and y directions, ratio of shear wall areas to the floor area, ratio of bays with infilled walls to total number bays and existence of open story were selected as parameters in GEP and ANFIS modeling. GEP and ANFIS have been widely used as alternative approaches to model complex systems. The effects of these parameters on base reactions and roof drift of RC frames were studied using 3D finite element method on 216 building models. Results obtained from 3D FEM models were used to in training and testing ANFIS and GEP models. In ANFIS and GEP models, number of floors, number of bays, ratio of shear walls and ratio of infilled bays were selected as input parameters, and base reactions and roof drifts were selected as output parameters. Results showed that the ANFIS and GEP models are capable of accurately predicting the base reactions and roof drifts of RC frames used in the training and testing phase of the study. The GEP model results better prediction compared to ANFIS model.