Membership function comparative investigation on productivity forecasting of solar still using adaptive neuro-fuzzy inference system approach

2017 ◽  
Vol 37 (1) ◽  
pp. 249-259 ◽  
Author(s):  
Ahmed F. Mashaly ◽  
A. A. Alazba
Keyword(s):  
Membership Function ◽  
Fuzzy Inference System ◽  
System Approach ◽  
Fuzzy Inference ◽  
Comparative Investigation ◽  
Solar Still ◽  
Inference System ◽  
Neuro Fuzzy

scholarly journals PREDIKSI TINGGI MUKA AIR BENDUNGAN RIAM KANAN MENGGUNAKAN ADAPTIVE NEURO FUZZY INFERENCE SYSTEM

2018 ◽  
Vol 9 (1) ◽  
pp. 11
Author(s):  
Yusri Ikhwani
Keyword(s):  
Membership Function ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Bendungan riam kanan yang berada kabupaten banjar ialah salah satu waduk terbesar di kalimantan selatan yang ada di aranio, kabupaten banjar. Waduk buatan yang dalam pembangunannya memakan waktu selama 10 tahun ini dibangun membendung 8 sungai yang bersumber dari Pegunungan Meratus. Tujuan utama dibangunnya waduk riam kanan adalah untuk membangun pembangkit listrik tenaga air untuk daerah kalimantan selatan dan sekitarnya.Tujuan penelitian ini ialah untuk memprediksi tinggi muka air bendungan riam kanan menggunakan metode Adaptive Neuro Fuzzy Inference System (ANFIS) agar dapat bermanfaat dalam kebijakan strategis ketahanan energi khususnya ketahanan pangan dan energi listrik, khususnya ketersediaan air untuk saluran irigasi.Perkiraan prediksi ini menggunakan data tinggi muka air bendungan riam kanan dari tahun 2009 sampai dengan 2015 yang didapatkan dari PLTU riam kanan provinsi kalimantan selatan. Prosedur memprediksi diawali dengan melakukan proses pembagian data, yaitu menjadi data pelatihan dan data pengujian. Setelah itu dilakukan penentuan variabel-variabel pendukung input yang memberikan korelasi cukup signifikan terhadap variabel output. Serelah itu melakukan proses pengujian dengan membandingkan 2 membership function untuk menentukan yang mana memiliki tingkat akurasi yang baik dan nilai error yang rendah dalam memprediksi tinggi muka air bendungan riam kanan.Hasilnya ialah prediksi tinggi muka air bendungan riam kanan menggunakan metode Adaptive Neuro Fuzzy Inference System (ANFIS) dengan membandingkan 2 membership function dengan tingkat keakuratan menghasilkan nilai RMSE 0,010065 pada membership function Bell Kata kunci: bendungan riam kanan, anfis, prediksi, tinggi muka air, membership fungtion

scholarly journals Rainfall Forecasting in Banyuwangi Using Adaptive Neuro Fuzzy Inference System

2017 ◽  
Vol 1 (2) ◽  
pp. 65 ◽  
Author(s):  
Gusti Ahmad Fanshuri Alfarisy ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Membership Function ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Hybrid Learning ◽  
Learning Method ◽  
Rainfall Forecasting ◽  
Inference System ◽  
Previous Event ◽  
Neuro Fuzzy ◽  
High Prediction

Rainfall forcasting is a non-linear forecasting process that varies according to area and strongly influenced by climate change. It is a difficult process due to complexity of rainfall trend in the previous event and the popularity of Adaptive Neuro Fuzzy Inference System (ANFIS) with hybrid learning method give high prediction for rainfall as a forecasting model. Thus, in this study we investigate the efficient membership function of ANFIS for predicting rainfall in Banyuwangi, Indonesia. The number of different membership functions that use hybrid learning method is compared. The validation process shows that 3 or 4 membership function gives minimum RMSE results that use temperature, wind speed and relative humidity as parameters.

scholarly journals Detection of waterborne bacteria using Adaptive Neuro-Fuzzy Inference System

2020 ◽  
Vol 158 ◽  
pp. 05002
Author(s):  
Farhan Mohammad Khan ◽  
Smriti Sridhar ◽  
Rajiv Gupta
Keyword(s):  
Water Quality ◽  
Membership Function ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Quality Analysis ◽  
Hybrid Technique ◽  
Membership Functions ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques

The detection of waterborne bacteria is crucial to prevent health risks. Current research uses soft computing techniques based on Artificial Neural Networks (ANN) for the detection of bacterial pollution in water. The limitation of only relying on sensor-based water quality analysis for detection can be prone to human errors. Hence, there is a need to automate the process of real-time bacterial monitoring for minimizing the error, as mentioned above. To address this issue, we implement an automated process of water-borne bacterial detection using a hybrid technique called Adaptive Neuro-fuzzy Inference System (ANFIS), that integrates the advantage of learning in an ANN and a set of fuzzy if-then rules with appropriate membership functions. The experimental data as the input to the ANFIS model is obtained from the open-sourced dataset of government of India data platform, having 1992 experimental laboratory results from the years 2003-2014. We have included the following water quality parameters: Temperature, Dissolved Oxygen (DO), pH, Electrical conductivity, Biochemical oxygen demand (BOD) as the significant factors in the detection and existence of bacteria. The membership function changes automatically with every iteration during training of the system. The goal of the study is to compare the results obtained from the three membership functions of ANFIS- Triangle, Trapezoidal, and Bell-shaped with 35 = 243 fuzzy set rules. The results show that ANFIS with generalized bell-shaped membership function is best with its average error 0.00619 at epoch 100.

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