Prediction of Safety Factor for Slope Designed with Various Limit Equilibrium Methods

2011 ◽  
Vol 462-463 ◽  
pp. 611-615 ◽  
Author(s):  
Tariq Mohamed ◽  
Anuar Kasa ◽  
Taha Mohd Raihan
Keyword(s):  
Safety Factor ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Limit Equilibrium ◽  
Calculated Data ◽  
Unit Weight ◽  
Inference System ◽  
Limit Equilibrium Methods ◽  
Neuro Fuzzy ◽  
The Stability

The purpose of this study is to predict the stability of slope using adaptive neuro fuzzy inference system (ANFIS). Based on limit equilibrium theory, four different methods of analyses, i.e. Morgenstern-Price, Janbu, Bishop and Ordinary were used to calculate the overall safety factor of various slope designs. Neuro-fuzzy inference system was used to map from a given input to an output. Important parameters such as height of slope (H), unit weight of soil (γ), angle of slope (θ), coefficient of cohesion (c) and internal angle of friction (ф) were used as the input parameters while overall safety factor was the output. ANFIS model to predict the stability of the slopes was generated from the calculated data. Results showed that factors of safety predicted using ANFIS agreed well with factors of safety calculated using Limit Equilibrium Methods (LEM).

Application of Fuzzy Set Theory to Evaluate the Stability of Slopes

2014 ◽  
Vol 580-583 ◽  
pp. 566-571
Author(s):  
Tarig Mohamed ◽  
Anuar Kasa
Keyword(s):  
Membership Function ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Limit Equilibrium ◽  
Unit Weight ◽  
Good Precision ◽  
Inference System ◽  
Limit Equilibrium Methods ◽  
Stability Of Slopes ◽  
The Stability

An artificial intelligence tools, Adaptive Neuro Fuzzy Inference System (ANFIS), was used in this study to predict the stability of slopes. Data used in this study were 300 various designs of slope. Those designs were created by using Slope/W which calculated factors of safety using various limit equilibrium methods (LEM) such as Bishop, Spencer and Morgenstern-Price. The input parameters consisted of height of slope, H (1–10 m), unit weight of slope material, γ (15-22 kN/m3), angle of slope, θ (11.31°-78.69°), coefficient of cohesion, c (0-50 kN/m2) and internal angle of friction, (20°- 40°) and the output parameter is the factor of safety. To build the fuzzy inference system, 243 rules were used at 60 epochs. The number of membership function for the any input was three and the type of membership function for output was linear. ANFIS obtained regression square (R2) of one for Bishop, one for Janbu, one for Morgenstern-Price and one for Ordinary. The result proved that ANFIS may possibly predict the safety factor with good precision and nearly to the target data.

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.

PENGEMBANGAN SISTEM MANAJEMEN PENDUKUNG KEPUTUSAN PENILAIAN MUTU KEPEGAWAIAN DINAS PENDIDIKAN REMBANG MENGGUNAKAN ALGORITMA ANFIS (ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM)

2020 ◽  
Vol 12 (1) ◽  
pp. 6-17
Author(s):  
Angga debby frayudha ◽  
Aris Yulianto ◽  
Fatmawatul Qomariyah
Keyword(s):  
Fuzzy Inference System ◽  
Industry 4.0 ◽  
Fuzzy Inference ◽  
System Model ◽  
Inference System ◽  
Neuro Fuzzy

Di era revolusi industry 4.0 terdapat banyak sekali kemudahan yang diberikan teknologi kepada manusia. Tentu ini akan menjadi baik apabila manusia mampu memanfaatkan hal tersebut dengan baik pula. Namun disisi lain juga bisa mengakibatkan dampak negative terhadap manusia, misalnya dengan adanya internet bisa mengakibatkan manusia melakukan penipuan di media social. Selain itu dengan canggihnya teknologi dapat menjadikan manusia menjadi malas yang bisa berimbas menurunnya kualitas sumber daya manusia. Maka dari itu untuk menghadapi hal ini perlu menyiapkan pendidikan yang baik.Pendidikan akan berjalan baik apabila lembaga yang mengurusnya berkompeten dalam melakukan tugasnya .Penulis coba memberikan ide untuk memprediksi kinerja pegawai Dinas Pendidikan Kabupaten Rembang menggunakan mentode ANFIS (Adaptive Neuro Fuzzy Inference System) guna untuk membantu lembaga tersebut menyeleksi maupun menilai kinerja karyawan demi meningkatkan kualitas dari segi sumber daya manusia. ANFIS merupakan jaringan adaptif yang berbasis pada sistem kesimpulan fuzzy (fuzzy inference system). Model penilaian kinerja pegawai di Dinas Pendidikan Kabupaten Rembang dengan menggunakan Adaptive Neuro-Fuzzy Inference System (ANFIS) menghasilkan penilaian  yang lebih baik dan akurat.  Hasil pengujian metode tersebut memiliki nilai akurasi 65%. Dengan metode ANFIS (Adaptive Neuro Fuzzy Inference System) dapat memprediksi kinerja karyawan sebagai salah satu pengambilan keputusan terhadap kinerja pegawai. Selain itu nantinya system penlaian kinerja pegawai akan lebih tertata dan efisien.

scholarly journals Global Image Thresholding Adaptive Neuro-Fuzzy Inference System Trained with Fuzzy Inclusion and Entropy Measures

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 286 ◽  
Author(s):  
Athanasios Bogiatzis ◽  
Basil Papadopoulos
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Initial Step ◽  
Local Area ◽  
Mathematical Functions ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Entropy Measures ◽  
Complex Procedure ◽  
Artificial Neural Network Ann

Thresholding algorithms segment an image into two parts (foreground and background) by producing a binary version of our initial input. It is a complex procedure (due to the distinctive characteristics of each image) which often constitutes the initial step of other image processing or computer vision applications. Global techniques calculate a single threshold for the whole image while local techniques calculate a different threshold for each pixel based on specific attributes of its local area. In some of our previous work, we introduced some specific fuzzy inclusion and entropy measures which we efficiently managed to use on both global and local thresholding. The general method which we presented was an open and adaptable procedure, it was free of sensitivity or bias parameters and it involved image classification, mathematical functions, a fuzzy symmetrical triangular number and some criteria of choosing between two possible thresholds. Here, we continue this research and try to avoid all these by automatically connecting our measures with the wanted threshold using some Artificial Neural Network (ANN). Using an ANN in image segmentation is not uncommon especially in the domain of medical images. However, our proposition involves the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) which means that all we need is a proper database. It is a simple and immediate method which could provide researchers with an alternative approach to the thresholding problem considering that they probably have at their disposal some appropriate and specialized data.

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