Neuro Fuzzy(뉴로 퍼지)란 무엇입니까?
Neuro Fuzzy 뉴로 퍼지 - In this article, the neuro fuzzy with binary cuckoo search optimization method is proposed for detecting tumors on MR images. [1] The structure of the proposed MRAS consists of Neuro fuzzy (NF) controller and an adaptive system based on sliding mode controller (SMC). [2] A control strategy was also developed for the first time in the field of phase equilibrium by using the neuro fuzzy inverse approach (ANFISi) to estimate pure component properties from the solubility data without passing through GCM methods. [3] In this work, neuro fuzzy-based CHD risk prediction is performed after performing pre-processing and HRV feature extraction. [4] The three methods are linear regression, polynomial regression, and neuro fuzzy. [5]본 논문에서는 자기공명영상에서 종양을 검출하기 위한 바이너리 뻐꾸기 검색 최적화 방법을 이용한 신경 퍼지를 제안한다. [1] 제안하는 MRAS의 구조는 NF(Neuro fuzzy) 제어기와 SMC(sliding mode controller) 기반의 적응형 시스템으로 구성된다. [2] 에 대한 통제 전략도 개발되었다. 뉴로퍼지를 이용한 위상평형 분야 최초 역접근법(ANFISi)을 사용하여 순수한 구성요소 속성을 추정합니다. GCM 방법을 거치지 않고 용해도 데이터. [3] 본 연구에서는 전처리 및 HRV 특징 추출을 수행한 후 신경 퍼지 기반 CHD 위험 예측을 수행합니다. [4] 세 가지 방법은 선형 회귀, 다항식 회귀 및 신경 퍼지입니다. [5]
artificial neural network 인공 신경망
This study presents the development of new empirical prediction models to evaluate swell pressure and unconfined compression strength of expansive soils (PsUCS-ES) using three soft computing methods, namely artificial neural networks (ANNs), adaptive neuro fuzzy inference system (ANFIS), and gene expression programming (GEP). [1] The forward and the inverse problem of a thin, circular, loop antenna that radiates in free space is modeled and solved by using soft computing techniques such as artificial neural networks and adaptive neuro fuzzy inference systems. [2] A comparative study is performed for various AI based MPPT techniques such as Fuzzy, Artificial Neural Network (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS). [3] The main objective of this paper is to develop models for the simulation of pan-evaporation with the help of Penman and Hamon’s equations, Artificial Neural Networks (ANNs), and the Artificial Neuro Fuzzy Inference System (ANFIS). [4] This paper investigates the simultaneous use of in-situ hydrologic measurements in combination with two different AI methods, namely, Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), for developing enhanced long-term streamflow forecasting models. [5] : Neuro Fuzzy is a hybrid system that combines Artificial Neural Networks with Fuzzy Logic. [6] We have applied Multilayer Perceptron-Artificial Neural Networks (MLP-ANN), Hybrid-Adaptive Neuro Fuzzy Inference System (Hybrid-ANFIS), Particle Swarm Optimization-Adaptive Neuro Fuzzy Inference System (PSO-ANFIS) and Coupled Simulated Annealing-Least Squares Support Vector Machine (CSA-LSSVM). [7] Artificial Neural Network (ANN) and Neuro Fuzzy Classifier (NFC) are employed to evaluate the performance of the proposed metric. [8] This paper shows a comparison of Adaptive Neuro Fuzzy Inference System (ANFIS) with Instantaneous Reactive Power Theory (IRPT) and Artificial Neural Network (ANN) for a three phase Distribution Static Compensator (DSTATCOM). [9] Artificial intelligence technology and its related applications such as; artificial neural network (ANN), support vector machine (SVM) and adaptive neuro fuzzy interference system (ANFIS) will be utilized to predict and estimate accurate wellbore torque which will be applied effectively to prevent real time stuck pipe situation through a friendly user software which will maintain the downhole torque within the SAFE zone by controlling the unified surface drilling variables such as; weight on bit (WOB), Rate of Penetration (ROP) and Flow Rate. [10] This paper investigates the simultaneous use of in-situ hydrologic measurements in combination with two different artificial intelligent (AI) methods, namely, Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), for developing enhanced long-term streamflow forecasting models. [11] In this study, an artificial neural network (ANN) and adaptive neuro fuzzy interface system (ANFIS) were employed to model the compressive strength of natural volcanic ash mortar (VAM) by using the six-fold symmetry of concrete failure. [12] Eight process parameters are considered as input parameters as per the plant maintenance team's recommendations and soft computing methods, artificial neural networks and adaptive neuro fuzzy inference system are employed and a statistical regression tool, autoregressive integrated moving average, is also applied for comparison. [13] Moreover, the statistical measures of proposed method are also determined and compared with the existing method as Artificial Neural Network (ANN), Mayfly algorithm with Particle Swarm Optimization (MA-PSO), Recurrent Neural Network -PSO (RNN-PSO) and Adaptive Neuro Fuzzy Inference System (ANFIS) respectively. [14] “Regression” (Reg), “Artificial Neural Networks” (ArtNN), “Adaptive Neuro Fuzzy Inference System” (ANFIS), “Ensemble of Trees” (EnT), and “Support Vector Regression” (SuVR). [15] The advancement in computational methods is discussed for forward and inverse models along with Optimization techniques using Artificial Neural Networks (ANN), Genetic Algorithm (GA) and Artificial Neuro Fuzzy Inference System (ANFIS). [16] The results show that Artificial Neural Network (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS), and regression learner are best methods for this issue with RMSE 0. [17] The results of artificial intelligence (AI) based models namely artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) were presented. [18] Likewise, the proposed method is analysed with factual measures, for example, the root mean square error, mean absolute percentage error, mean bias error, and consumption time; and the execution is evaluated by utilizing the examination at various strategies like artificial neural network, fuzzy, and adaptive neuro fuzzy inference system techniques. [19]본 연구에서는 인공신경망(ANN), 적응형 신경 퍼지 추론 시스템(ANFIS), 팽창성 토양(PsUCS-ES)의 세 가지 소프트 컴퓨팅 방법을 사용하여 팽창 압력과 팽창하지 않은 토양의 압축 강도를 평가하기 위한 새로운 경험적 예측 모델의 개발을 제시합니다. 유전자 발현 프로그래밍(GEP). [1] 자유 공간에서 방사하는 얇은 원형 루프 안테나의 순방향 및 역방향 문제는 인공 신경망 및 적응형 신경 퍼지 추론 시스템과 같은 소프트 컴퓨팅 기술을 사용하여 모델링되고 해결됩니다. [2] nan [3] 이 논문의 주요 목적은 Penman과 Hamon의 방정식, 인공 신경망(ANN) 및 인공 신경 퍼지 추론 시스템(ANFIS)의 도움으로 팬 증발 시뮬레이션을 위한 모델을 개발하는 것입니다. [4] nan [5] nan [6] nan [7] nan [8] nan [9] nan [10] nan [11] nan [12] nan [13] nan [14] nan [15] nan [16] nan [17] nan [18] nan [19]
support vector machine 지원 벡터 기계
In addition to available correlations and implementation of ANNs, other intelligent approaches such as support vector machine and adaptive neuro fuzzy interface system are also applicable for accurate modeling of rheological properties of hybrid nanofluids. [1] In order to accomplish this aim, support-vector machine, Adaptive neuro fuzzy inference system and Artificial neural network models have been evaluated for different input combinations. [2] To predict the noise level, adaptive neuro fuzzy inference system (ANFIS) has been developed and a detailed comparative analysis has been performed with conventional soft-computing techniques such as neural networks (NN), generalized linear model (GLM), random forests (RF), Decision Trees and Support Vector Machine (SVM). [3] The ML algorithms namely support vector machine (SVM) with linear, polynomial, radial and sigmoid kernel, random forest (RF), multi-layer perceptron (MLP), radial basis function (RBF), Wang and Mendel’s (WM), subtractive clustering (SBC), adaptive neuro fuzzy inference system (ANFIS), hybrid fuzzy interference system (HyFIS), and dynamic evolving neural fuzzy inference system (DENFIS) were used. [4] In this study, three models namely Least Square Support Vector Machine optimized by Coupled Simulated Annealing optimization algorithm (CSA-LSSVM), Genetic Programming (GP) and Adaptive-Neuro Fuzzy Inference System optimized by PSO, and GA methods (PSO-ANFIS and GA-ANFIS) were applied to estimate Δ H C ∘ Also, Δ H C ∘ can be expressed by the GP model with an equation. [5] Therefore, in the current study to cope with this issue and alleviate the uncertainty, Least Squares Support Vector Machine (LSSVM) and Adaptive-Neuro Fuzzy Inference System (ANFIS) algorithms cooperating with the particle swarm optimization (PSO) were suggested as a suitable method to increase the precision of estimating geochemical factors. [6] The predictive results were compared with other predictive methods including random forest (RF), support vector machines (SVM), artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and IT2FLS with parameters generated by using the fuzzy c-means algorithm (IT2FLS-FCM). [7] Three Machine Learning techniques - Support Vector Machine, Artificial Neural Network and Adaptive Neuro Fuzzy Inference System with and without the feature reduction technique of Principle Component Analysis were modelled and evaluated, the best of which was selected to model the proposed prediction model. [8]ANN의 사용 가능한 상관 관계 및 구현 외에도 지원 벡터 기계 및 적응형 신경 퍼지 인터페이스 시스템과 같은 다른 지능형 접근 방식도 하이브리드 나노유체의 유변학적 특성의 정확한 모델링에 적용할 수 있습니다. [1] 이 목표를 달성하기 위해 지원 벡터 머신, 적응형 신경 퍼지 추론 시스템 및 인공 신경망 모델이 다양한 입력 조합에 대해 평가되었습니다. [2] nan [3] nan [4] nan [5] nan [6] nan [7] nan [8]
particle swarm optimization 입자 떼 최적화
Also, the proposed method is utilized in training Adaptive Neuro Fuzzy Inference System (ANFIS) classifier and the results obtained from using IPO, SIPO, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms in the training of this classifier are compared. [1] coupled particle swarm optimization- adaptive neuro fuzzy inference system is used to simulate runoff in the structure of a multi-objective metaheuristic optimization. [2] Also considering the performance metric, the Meta-ELM model provides the best results in all regions and the Levenberg–Marquardt algorithm -feed forward neural network and adaptive neuro fuzzy inference system -particle swarm optimization show competitive results for forecasting process. [3] As tool wear is the main factor that affects the quality of machined surface, in this study, we proposed an intelligent model-adaptive neuro fuzzy inference system (ANFIS) to estimate the tool wear, and ANFIS was learned by the improved particle swarm optimization (PSO) algorithm. [4] Application of adaptive neuro fuzzy inference system (ANFIS)-based particle swarm optimization (PSO) algorithm to the problem of aerodynamic modeling and optimal parameter estimation for aircraft has been addressed in this chapter. [5] This paper presents novel hybrid machine learning models, namely Adaptive Neuro Fuzzy Inference System optimized by Particle Swarm Optimization (PSOANFIS), Artificial Neural Networks optimized by Particle Swarm Optimization (PSOANN), and Best First Decision Trees based Rotation Forest (RFBFDT), for landslide spatial prediction. [6]또한 제안한 방법을 ANFIS(Adaptive Neuro Fuzzy Inference System) 분류기 훈련에 활용하고 이 분류기 훈련에서 IPO, SIPO, GA(Genetic Algorithm) 및 PSO(Particle Swarm Optimization) 알고리즘을 사용하여 얻은 결과를 비교합니다. [1] 결합 입자 떼 최적화 - 적응형 신경 퍼지 추론 시스템은 다중 목표 메타휴리스틱 최적화의 구조에서 유출을 시뮬레이션하는 데 사용됩니다. [2] nan [3] nan [4] nan [5] nan [6]
multiple linear regression 다중 선형 회귀
A neural network (ANN), an adaptive neuro fuzzy inference system (ANFIS), and multiple linear regression (MLR) models were employed as the downscaling models. [1] multiple linear regression (MLR), artificial neural network (ANN), and adaptive neuro fuzzy inference system (ANFIS) were applied to the multi-national WBE dataset. [2] The five developed models in this study are multilayer perceptron (MLP) neural network, radial basis functions (RBF) neural network, adaptive neuro fuzzy inference systems (ANFIS), multiple linear regression (MLR), and multiple non-linear regression (MNLR) in comparison with empirical equations. [3]축소 모델로는 신경망(ANN), 적응형 신경 퍼지 추론 시스템(ANFIS) 및 다중 선형 회귀(MLR) 모델을 사용했습니다. [1] 다중 선형 회귀(MLR), 인공 신경망(ANN) 및 적응형 신경 퍼지 추론 시스템(ANFIS)이 다국적 WBE 데이터 세트에 적용되었습니다. [2] nan [3]
fuzzy inference system 퍼지 추론 시스템
This approach is realized and examined on the Adaptive Neural-fuzzy Inference System (ANFIS) and Meta-cognitive neuro fuzzy inference system (McFIS). [1] Four pattern recognition algorithms—adaptive neuro fuzzy inference system (ANFIS), dynamic evolving neural-fuzzy inference system (DENFIS), bagging and deep belief network (DBN)—are then used on the decomposed components to obtain granular level forecasts. [2] There are numerous approaches in the literature on machinery noise prediction based on statistical models, soft computing techniques such as fuzzy inference system, artificial neural networks, support vector machines, adaptive neuro fuzzy inference system and other classification methods. [3]이 접근법은 ANFIS(Adaptive Neural-Fuzzy Inference System) 및 McFIS(메타인지 신경 퍼지 추론 시스템)에서 구현 및 검사됩니다. [1] 그런 다음 4가지 패턴 인식 알고리즘(적응형 신경 퍼지 추론 시스템(ANFIS), 동적 진화 신경 퍼지 추론 시스템(DENFIS), 배깅 및 DBN)을 분해된 구성 요소에 사용하여 세분화된 수준의 예측을 얻습니다. [2] nan [3]
sliding mode control 슬라이딩 모드 제어
In this research work, a neuro fuzzy based adaptive integral super twisting sliding mode control (SMC) have been simulated in Matlab/Simulink. [1] The proposed ANFSMC-PSS is a combination of sliding mode control (SMC) and neuro fuzzy system (NFS) to enhance stability of power system. [2] Those intelligent techniques are adaptive neuro fuzzy interference system (ANFIS) and neuro-sliding mode control scheme. [3]이 연구 작업에서 신경 퍼지 기반 적응 적분 슈퍼 트위스팅 슬라이딩 모드 제어(SMC)는 Matlab/Simulink에서 시뮬레이션되었습니다. [1] 제안하는 ANFSMC-PSS는 전력계통의 안정성을 향상시키기 위해 슬라이딩 모드 제어(SMC)와 신경 퍼지 시스템(NFS)의 조합이다. [2] nan [3]
direct matrix converter 직접 행렬 변환기
This paper presents the modeling, design, and simulation of an adaptive neuro fuzzy inference strategy (ANFIS) for controlling the speed of the Double Star induction Machine (DSIM), the machine is fed by three phase direct matrix converter which makes directly AC-AC power conversion is modeled using Direct Space Vector Modulation technique(DSVM) for direct matrix converter. [1] This paper presents the modeling, design, and simulation of an adaptive neuro fuzzy inference strategy (ANFIS) for controlling the speed of the Double Star induction Machine (DSIM), the machine is fed by three phase direct matrix converter which makes directly AC-AC power conversion is modeled using Direct Space Vector Modulation technique(DSVM) for direct matrix converter. [2]이 논문은 DSIM(Double Star Induction Machine)의 속도를 제어하기 위한 적응형 신경 퍼지 추론 전략(ANFIS)의 모델링, 설계 및 시뮬레이션을 제시합니다. 전력 변환은 직접 매트릭스 변환기를 위한 DSVM(직접 공간 벡터 변조) 기술을 사용하여 모델링됩니다. [1] nan [2]
tunnel smooth blasting 터널 부드러운 발파
By using the methods of index utilization rate statistics, gray correlation analysis, and principal component analysis, this paper primarily elects and selects the control indexes; establishes the tunnel smooth blasting quality control index system; constructs a comprehensive optimization control model of tunnel smooth blasting quality using back propagation artificial neural network (BP-ANN), Elman neural network (ENN), and adaptive neuro fuzzy inference systems (ANFIS); and studies the tunnel smooth blasting quality control system. [1]본 논문에서는 지표이용률통계, 회색상관분석, 주성분분석 등의 방법을 이용하여 주로 통제지표를 선정한다. 터널 부드러운 발파 품질 관리 지표 시스템을 구축합니다. 역전파 인공 신경망(BP-ANN), 엘만 신경망(ENN) 및 적응형 신경 퍼지 추론 시스템(ANFIS)을 사용하여 터널 부드러운 발파 품질의 포괄적인 최적화 제어 모델을 구성합니다. 터널 부드러운 발파 품질 관리 시스템을 연구합니다. [1]