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 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 PREDIKSI PENGGUNA BUS TRANS SARBAGITA DENGAN METODE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM

2013 ◽  
Vol 2 (3) ◽  
pp. 46
Author(s):  
SLAMET SAMSUL HIDAYAT ◽  
I PUTU EKA NILA KENCANA ◽  
KETUT JAYANEGARA
Keyword(s):  
Least Squares ◽  
Public Transportation ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Hybrid Learning ◽  
Inference System ◽  
Transportation Services ◽  
Neuro Fuzzy ◽  
Two Phases

Trans Sarbagita is a public transportation services people at Denpasar, Badung, Gianyar and Tabanan. Trans Sarbagita is aimed to resolve a problems caused by accretion volume of vehicles in Bali. This study conducted to forecast the number of Trans Sarbagita passengers in 2013 using ANFIS. The ANFIS system composed by five layers where each layers has a different function and its divide in two phases, i.e. forward and backward phases. The ANFIS uses a hybrid learning algorithm which is a combination of Least Squares Estimator (LSE) on forwards phases and Error Backpropagation (EBP) on the backward phases. The results show, ANFIS with six inputs with M.F of  Pi  produces smallest error, compared to seven and eight input and M.F gauss and generalizedbell. Forecast of Trans Sarbagita passenger numbers in 2013 have to fluctuated every day and the average of passenger’s Trans Sarbagita for a day is 1627 passengers with MSE equal to 10210 and MAPE is 4.01%.

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.

Navigational strategy for underwater mobile robot based on adaptive neuro-fuzzy inference system model embedded with shuffled frog leaping algorithm–based hybrid learning approach

2017 ◽  
Vol 231 (4) ◽  
pp. 844-862
Author(s):  
DR Parhi ◽  
S Kundu
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Three Dimensional ◽  
Hybrid Learning ◽  
System Model ◽  
Underwater Robot ◽  
Shuffled Frog Leaping Algorithm ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Shuffled Frog Leaping

In this research article, a novel navigational approach has been introduced for underwater robot based on learning and self-adaptation ability of adaptive neuro-fuzzy inference system. For avoiding obstacles during three-dimensional navigation, two adaptive neuro-fuzzy inference system models have been coupled to find out required change in heading angles of underwater robot in horizontal and vertical planes, respectively. A new hybrid learning scheme has been proposed for adaptive neuro-fuzzy inference system. Here, memetic approach based shuffled frog leaping algorithm has been used to tune the premise parameters and consequent parameters has been estimated through recursive least square estimation. Minimization of error in output of adaptive neuro-fuzzy inference system model has been treated as major objective of evolutionary-based training algorithm. Preliminary robotic behaviors of underwater robot have been successfully executed by implementing such well-trained adaptive neuro-fuzzy inference system architecture within three-dimensional unspecified workspace. Navigational performance of adaptive neuro-fuzzy inference system trained with the proposed hybrid learning algorithm has been compared with other three-dimensional navigational approaches in simulation mode for authentication purpose. Experimental verification has also been carried out to validate the feasibility and efficiency of the proposed navigational strategy.

scholarly journals Penentuan Keputusan Pemberian Pinjaman Kredit Usaha Rakyat Menggunakan Metode Adaptive Neuro Fuzzy Inference System (Studi Kasus: BRI Unit A. Yani Bontang)

2020 ◽  
Vol 15 (2) ◽  
pp. 66
Author(s):  
Wahyu Dyan Permana ◽  
Indah Fitri Astuti ◽  
Heliza Rahmania Hatta
Keyword(s):  
Soft Computing ◽  
Membership Function ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Kredit Usaha Rakyat (KUR) merupakan program pemerintah yang termasuk dalam kelompok program penanggulangan kemiskinan berbasis pemberdayaan usaha ekonomi mikro dan kecil. Bank Rakyat Indonesia (BRI) unit A.Yani Bontang merupakan salah satu bank penyedia pemberian modal KUR yang pada 1 tahun terakhir kredit macet sebesar 1.2 % dari total pinjaman yang didistribusikan. Sistem Pendukung Keputusan (SPK) berbasis soft computing metode ANFIS dapat membantu masalah pemberian pinjaman dengan memberikan alternatif keputusan yang dapat membantu mengefesienkan waktu dalam pengambilan keputusan oleh bank. ANFIS merupakan sistem hybrid yang menggabungkan kelebihan antara sistem fuzzy dan jaringan syaraf tiruan. Variabel input yang digunakan adalah penghasilan, tempat tinggal, jumlah tanggungan, jaminan, serta lama usaha dan output adalah keputusan diterima atau ditolaknya pengajuan pinjaman oleh debitur. Hasil uji coba pelatihan mengunakan jenis membership function yang paling efektif adalah jenis Generalized Bell dengan hasil rata-rata error sebesar 8.3278 x10-7. Metode ANFIS dapat digunakan dalam memberikan keputusan pemberian KUR dengan baik sesuai dengan jenis membership function dan iterasi pada tahap pelatihan jaringan.

scholarly journals Monthly Average Rainfall Forecasting Based On An Adaptive Neuro-Fuzzy Inference System in Upper Brahmani Basin, Odisha, India

2022 ◽  
Author(s):  
M.Uma Maheswar Rao ◽  
Kanhu Charan Patra ◽  
Suvendu Kumar Sasmal
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Membership Functions ◽  
Climate Data ◽  
Rainfall Forecasting ◽  
Anfis Model ◽  
Inference System ◽  
Neural Network Learning ◽  
Neuro Fuzzy ◽  
Learning Capabilities

Abstract Floods disrupt human activities, resulting in the loss of lives and property of a region. Excessive rainfall is one of the reasons for flooding, especially in the downstream areas of a catchment. Because of its complexity, understanding and forecasting rainfall is incredibly a challenge. This study investigates the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) in predicting rainfall using several surface weather parameters as predictors. An ANFIS model is developed for forecasting rainfall over the Upper Brahmani Basin by using 30 years of climate data. A hybrid model with six membership functions gives the best forecast for an area. The suggested method blends neural network learning capabilities with language representations of fuzzy systems that are transparent. The application of ANFIS is to the upper Brahmani river basin is tried for the first time. The ANFIS model with various input structures and membership functions has been built, trained, and tested to evaluate the capability of the model. Statistical performance indices are used to evaluate the performance. Using the developed model, forecast is done for year 2021 – 2030.

scholarly journals DETEKSI TINGKAT KERUSAKAN SISTEM KELISTRIKAN PADA MOBIL MENGGUNAKAN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM

2021 ◽  
Vol 8 (1) ◽  
pp. 114
Author(s):  
Rizky Prabowo ◽  
Zuliana Nurfadlilah ◽  
Favorisen Rosyking Lumbanraja ◽  
Didik Kurniawan
Keyword(s):  
User Interface ◽  
Membership Function ◽  
Fuzzy Inference System ◽  
Interface Design ◽  
Fuzzy Inference ◽  
Production Capacity ◽  
Graphic User Interface ◽  
Electrical System ◽  
Inference System ◽  
Neuro Fuzzy

<p><em>The automotive industry in Indonesia has significant increase in the past decade. A famous car company opened a manufacturing branch to increase its production capacity in Indonesia. An increase in sales is directly proportional to an increase in service to customers. Damage on electrical system is the majority of modern car. Unfortunately, car users have minimal knowledge of car electricity. This article describes the technique of detecting the level of damage to a car's electrical system using the Adaptive Neuro-Fuzzy Inference System (Anfis) concept. As a case study in designing the system in question is the electrical system on the Toyota Avanza. Formation of a fuzzy inference system which is used for the system formation process through a GUI-based interface design (Graphic User Interface). The output of the system is a fuzzy analysis based on the membership function of the Gaussian, Triangular and Trapezoid methods to obtain an analysis of the level of damage to the electrical system on a Toyota Avanza. From the results of the system test for starter system, firewire system and lighting system,  it is concluded that the analysis of the level of damage to the electrical system on the car using Anfis based on the Gaussian membership function model is more accurate(reach 85%) in predicting the level of damage to the analyzed electrical system.</em></p><p><em><strong>Keywords</strong></em><em>: Anfis, Electrical System, Fuzzy Inference System, Toyota Avanza</em> </p><p><em>Industri otomotif di Indonesia mengalami peningkatan signifikan dalam kurun waktu satu dekade belakangan ini. Perusahaan mobil terkenal membuka pabrik manufaktur untuk meningkatkan kapasitas produksinya di Indonesia. Peningkatan penjualan berbanding lurus dengan peningkatan layanan kepada pelanggan. Kerusakan sistem kelistrikan merupakan kerusakan yang mayoritas dialami pengguna kendaraan mobil terbaru masa kini. Sayangnya, pengguna kendaraan mobil memiliki pengetahuan yang kurang tentang kelistrikan. Artikel ini mendeskripsikan tentang teknik mendeteksi tingkat kerusakan sistem kelistrikan mobil dengan menggunakan konsep Adaptive Neuro-Fuzzy Inference System (ANFIS). Sebagai studi kasus dalam mendesain sistem yang dimaksud adalah sistem kelistrikan pada Mobil Toyota Avanza. Pembentukan fuzzy inference system yang kemudian digunakan untuk proses pembentukan sistem melalui desain interface berbasis GUI (Graphic User Interface). Keluaran dari sistem yang dibuat adalah analisa fuzzy berdasarkan fungsi keanggotaan metode Gaussian, Triangular dan Trapezoid untuk mendapatkan analisa tingkat kerusakan sistem kelistrikan pada mobil Toyota Avanza. Dari hasil uji sistem yang dilakukan pada sistem starter, sistem pengapian dan sistem penerangan diperoleh kesimpulan analisis tingkat kerusakan sistem kelistrikan pada mobil dengan menggunakan Anfis berdasarkan model membership function Gaussian adalah lebih akurat (mencapai 85%) dalam menduga tingkat kerusakan sistem kelistrikan yang dianalisa.</em></p><p><em><strong>Kata kunci</strong></em><em>: Anfis; Fuzzy Inference System; Sistem Kelistrikan; Toyota Avanza</em></p>

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