scholarly journals Penggunaan ANFIS pada Pengaturan Debit Air Berdasarkan Volume Air Dalam Tangki

2020 ◽  
Vol 1 (1) ◽  
pp. 24-32
Author(s):  
Machrus Ali ◽  
Ruslan Hidayat ◽  
Iwan Cahyono
Keyword(s):  
Neural Network ◽  
Water Level ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Ball Valve ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Output Flow

Adaptive Neuro-Fuzzy Inference System (ANFIS) adalah penggabungan mekanisme Fuzzy Inference System (FIS) dan Neural Network (NN) yang digambarkan dalam arsitektur jaringan syaraf. Sistem inference fuzzy yang digunakan adalah sistem inference fuzzy model Tagaki-Sugeno-Kang (TSK) orde satu dengan pertimbangan kesederhanaan dan kemudahan komputasi. Pada penelitian ini sebagai pembanding didesain tanpa control, desain dengan PID standart, desain dengan Fuzzy Login Controller (FLC), dan ANFIS controller. Dalam desain penelitian ini yang dikontrol adalah ball valve electric pada tangki agar debit air yang keluar dari tangki sesuai dengan yang dibutuhkan dalam proses produksi dengan menggunakan empat control. Dari simulasi diapatkan bahwa Dsain Water Level yang paling baik pada percobaan ini adalah menggunakan metode ANFIS dengan nilai overshot dan undershot terkecil pada water level dan output flow. Sehingga desain ini bias dipakai acuan untuk menghasilkan control aliran air sesuai dengan harapan yang diinginkan. Hasil simulasi ini akan dibandingkan lagi dengan metode kecerdasan buatan yang lain, sehingga adan didapatkan hasil yang paling sesuai.

Model Identification of 3R Palnar Robot using Neural Network and Adaptive Neuro-Fuzzy Inference System

2021 ◽  
Author(s):  
R. Subasri ◽  
R. Meenakumari ◽  
R. Velnath ◽  
Srinivethaa Pongiannan ◽  
M. Sri Sai Mani Rohit Kumar
Keyword(s):  
Neural Network ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Model Identification ◽  
Inference System ◽  
Neuro Fuzzy

Estimation of current-induced scour depth around pile groups using neural network and adaptive neuro-fuzzy inference system

2009 ◽  
Vol 9 (2) ◽  
pp. 746-755 ◽  
Author(s):  
Mohammad Zounemat-Kermani ◽  
Ali-Asghar Beheshti ◽  
Behzad Ataie-Ashtiani ◽  
Saeed-Reza Sabbagh-Yazdi
Keyword(s):  
Neural Network ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Scour Depth ◽  
Pile Groups ◽  
Inference System ◽  
Neuro Fuzzy

scholarly journals Prediction of daily streamflow using artificial neural networks (ANNs), wavelet neural networks (WNNs), and adaptive neuro-fuzzy inference system (ANFIS) models

2020 ◽  
Vol 20 (4) ◽  
pp. 1396-1408
Author(s):  
Hüseyin Yıldırım Dalkiliç ◽  
Said Ali Hashimi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Water Resources ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Hydrological Processes ◽  
Inference System ◽  
Daily Streamflow ◽  
Neuro Fuzzy ◽  
Artificial Neural

Abstract In recent years, the prediction of hydrological processes for the sustainable use of water resources has been a focus of research by scientists in the field of hydrology and water resources. Therefore, in this study, the prediction of daily streamflow using the artificial neural network (ANN), wavelet neural network (WNN) and adaptive neuro-fuzzy inference system (ANFIS) models were taken into account to develop the efficiency and accuracy of the models' performances, compare their results and explain their outcomes for future study or use in hydrological processes. To validate the performance of the models, 70% (1996–2007) of the data were used to train them and 30% (2008–2011) of the data were used to test them. The estimated results of the models were evaluated by the root mean square error (RMSE), determination coefficient (R2), Nash–Sutcliffe (NS), and RMSE-observation standard deviation ratio (RSR) evaluation indexes. Although the outcomes of the models were comparable, the WNN model with RMSE = 0.700, R2 = 0.971, NS = 0.927, and RSR = 0.270 demonstrated the best performance compared to the ANN and ANFIS models.

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