Fuzzy membership function design: An adaptive neuro-fuzzy inference system (ANFIS) based approach

Kabir, Monika; Kabir, Mir Md. Jahangir

doi:10.1109/iccci50826.2021.9402633

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…Figure 11 gives the trajectory comparison of three commonly used membership function types in ANFIS, including Trimf, Gbellmf ,and Trapmf, which are described in detail in [29]. The statistical positioning errors of Trimf, Gbellmf, and Trapmf are 7.0102 m, 70.8683 m, and 71.5888 m, respectively.…”

Section: Performance Analysis Of Anfis-ekfmentioning

confidence: 99%

ANFIS-EKF-Based Single-Beacon Localization Algorithm for AUV

Zhao

Liu

et al. 2022

Remote Sensing

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Singe-beacon localization technology can help Autonomous Underwater Vehicles (AUVs) to obtain precise positions by deploying only one beacon. It is considered as a promising way, benefiting from saving much time and labor compared with traditional Long-Baseline Localization (LBL). A typical single-beacon localization scheme contains two essential questions: the initial observability problem and long-endurance trajectory tracking problem. Aiming at these core problems, a comprehensive solution for single-beacon localization is described in this paper. An multi-hypothesis initial position discriminant method is proposed firstly, which helps to achieve accurate initial location based on observability analysis. Then, an Adaptive Network Fuzzy Inference System (ANFIS)-improved Extended Kalman Filter (EKF) method is proposed, in which single-beacon measuring information is fused with off-the-shelf sensors, including DVL, Compass, etc. ANFIS-EKF can help to improve trajectory tracking precisions by restraining the heavy loss of linearization in conventional EKF. Both simulation and field tests are conducted to verify the performance of the proposed algorithms.

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Section: Performance Analysis Of Anfis-ekfmentioning

confidence: 99%

ANFIS-EKF-Based Single-Beacon Localization Algorithm for AUV

Zhao

Liu

et al. 2022

Remote Sensing

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“…Çalışmada hidrolojik zaman serilerinin modellemesinde başarılı şekilde kullanılan Takagi-Sugeno tipi ANFIS yapısı oluşturulmuştur [19]. Ağın efektif için önemli parametrelerden olan Üyelik Fonksiyonu (ÜF) tipine karar vermek üzere literatürde yaygın şekilde kullanılan Genelleştirilmiş Çan, Gauss ve Üçgen tip fonksiyonlar denenmiştir [20] Şekil 1. Doğu Karadeniz Havzası ve D22A090 AGİ konumu…”

Section: Adaptif Sinirsel Bulanık çıKarım Sistemi (Anfis)unclassified

Performance Evaluation of Artificial Intelligence and Heuristic Regression Methods for Rainfall-Runoff Modelling: An Application in Aksu Stream

BABACAN

Yüksek

Saka

2022

NÖHÜ Müh. Bilim. Derg.

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In this study, taking into account the Aksu Stream data, daily total precipitation (P) and daily mean flow (Q) values were using time lagged, 8 different Rainfall-Runoff models were created and runoff value estimated for the future. The Rainfall-Runoff models have been tried with different methods and this methods performances compared for Rainfall-Runoff process. Artificial Intelligence (AI) methods, Artificial Neural Networks (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS) and Heuristic Regression (HR) methods, Multivariate Adaptive Regression Splines (MARS) and Support Vector Machine (SVM) were used for describing the Rainfall-Runoff modelling. The performance of the methods is determined and compared with the Root Mean Square Error (RMSE), Correlation Coefficient (R) and Mean Absolute Error (MAE) coefficients. Although AI methods performance was very close, the lowest error value was obtained in the Rainfall-Runoff model created with the ANFIS method (RMSE=3.682, R=0.934, MAE=1.103). In the HR methods, the highest performance was observed on the Rainfall-Runoff model created with MARS (RMSE=3,101, R=0,952, MAE=1,302). In the performance evaluation, it was seen that HR methods have higher performance than AI methods for modelling Rainfall-Runoff process.

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