Software fault prediction using Mamdani type fuzzy inference system

2016 ◽  
Vol 8 (1) ◽  
pp. 14 ◽  
Author(s):  
Ezgi Erturk ◽  
Ebru Akcapinar Sezer
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Fault Prediction ◽  
Inference System ◽  
Software Fault Prediction ◽  
Software Fault

Application of ANFIS in Engine Fault Prediction

2014 ◽  
Vol 651-653 ◽  
pp. 651-654
Author(s):  
Hong Bin Zhang ◽  
Yan Fang Huang ◽  
Shi Xia Sun ◽  
Yang Zhao
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Fault Prediction ◽  
Prediction Theory ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Safety And Reliability ◽  
Engine Maintenance

Application of Adaptive Neuro-Fuzzy Inference System (ANFIS) in engine fault prediction is introduced in this paper. For insuring the engine safety and reliability, the fault of engine must be predicted, and the fault can be terminated in time. So, the fault prediction theory and the method of ANFIS are studied. The fault prediction sets, the structure and parameters for ANFIS are given in the course of fault prediction. It has made very pleased result through experiment of certain type engine fault prediction, and it is important to engine maintenance. The application of ANFIS has a good reference to engine fault prediction researching.

scholarly journals Smart Prediction Method of Software Defect Using Neuro-Fuzzy Approach

2018 ◽  
Vol 7 (2) ◽  
pp. 6-10
Author(s):  
Sunil Kumar Singh ◽  
Raj Shree
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Prediction Method ◽  
Inference System ◽  
Software Failure ◽  
Neuro Fuzzy ◽  
Software Program ◽  
Software Fault ◽  
Data Program ◽  
Program Reliability

Faults in software program structures continue to be a primary problem. A software fault is a disorder that reasons software failure in an executable product. A form of software fault predictions techniques were proposed, however none has proven to be continually correct. So, on this examine the overall performance of the Adaptive Neuro Fuzzy Inference System (ANFIS) in predicting software program defects and software program reliability has been reviewed. The datasets are taken from NASA Metrics Data Program (MDP) statistics repository. In the existing work a synthetic intelligence technique viz. Adaptive Neuro Fuzzy Inference System (ANFIS) goes for use for software disorder prediction.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

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