scholarly journals An Adaptive Neuro-Fuzzy-based Multisensor Data Fusion applied to real-time UAV autonomous navigation

2018 ◽  
Author(s):  
Ângelo de C. Paulino ◽  
Elcio H. Shiguemori ◽  
Lamartine N. F. Guimarães
Keyword(s):  
Data Fusion ◽  
Autonomous Navigation ◽  
Fuzzy Inference ◽  
Computational Cost ◽  
Position Estimation ◽  
Inference System ◽  
Positioning Systems ◽  
Multisensor Data Fusion ◽  
Safe Navigation ◽  
Neuro Fuzzy

The world trend in employing UAVs and drones is remarkable. The main reasons are that they may cost fractions of manned aircraft and avoid the exposure of human lives to risks. However, they depend on positioning systems that may be fallible. Therefore, it is necessary to ensure that these systems are as accurate as possible, aiming at safe navigation. In pursuit of this end, conventional Data Fusion techniques can be employed. Nonetheless, its high computational cost may be prohibitive due to the low payload of some UAVs. This paper proposes a Data Fusion application based on Computational Intelligence – Adaptive-Network-Based Fuzzy Inference System (ANFIS) – which is able to improve the accuracy of such position estimation systems.

scholarly journals Hybrid Adaptive Computational Intelligence-based Multisensor Data Fusion applied to real-time UAV autonomous navigation

2019 ◽  
Vol 22 (63) ◽  
pp. 162-195
Author(s):  
Ângelo De Carvalho Paulino ◽  
Lamartine Nogueira Frutuoso Guimarães ◽  
Elcio Hideiti Shiguemori
Keyword(s):  
Data Fusion ◽  
Real Time ◽  
Computational Intelligence ◽  
Autonomous Navigation ◽  
Fuzzy Inference ◽  
Computational Cost ◽  
Inference System ◽  
Positioning Systems ◽  
Multisensor Data Fusion ◽  
Multisensor Data

Nowadays, there is a remarkable world trend in employing UAVs and drones for diverse applications. The main reasons are that they may cost fractions of manned aircraft and avoid the exposure of human lives to risks. Nevertheless, they depend on positioning systems that may be vulnerable. Therefore, it is necessary to ensure that these systems are as accurate as possible, aiming to improve the navigation. In pursuit of this end, conventional Data Fusion techniques can be employed. However, its computational cost may be prohibitive due to the low payload of some UAVs. This paper proposes a Multisensor Data Fusion application based on Hybrid Adaptive Computational Intelligence - the cascaded use of Fuzzy C-Means Clustering (FCM) and Adaptive-Network-Based Fuzzy Inference System (ANFIS) algorithms - that have been shown able to improve the accuracy of current positioning estimation systems for real-time UAV autonomous navigation. In addition, the proposed methodology outperformed two other Computational Intelligence techniques.

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.

Penerapan Metode ANFIS untuk Prediksi Nilai Akhir dan Kelulusan Uji Kompetensi

2018 ◽  
Vol 4 (1) ◽  
pp. 21-28
Author(s):  
Rayendra
Keyword(s):  
Word Processing ◽  
Fuzzy Inference ◽  
Percentage Error ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Information And Communication ◽  
Anfis Method ◽  
Final Score ◽  
Computer Literate ◽  
Competence Test

To improve the graduation of Computer Literate Certified Professional (CLCP) competence test conducted by Competence Test of Information and Communication Technology (TUK-TIK) needs to be done continuous improvement by increasing try out competency test. Past values of the competency test can be used as modeling to predict the final score and the passing of the competency test. With the modeling can be predicted the passing of competency test participants through try out-try out done so that can be known weakness of candidate competency test from three units of CLCP competence. The modeling used to predict the final score and the passing of this competency test is the Adaptive Neuro Fuzzy Inference System (ANFIS) method. Used 20 past data of competency test participants with 6 criteria as input value from three CLCP competence units namely Word Processing, Spreadsheet, and Presentation. The resulting prediction is accurate enough with MAPE (Mean Absolute Percentage Error) value for each competency unit of 0.31492%, 0.284202%, and 0.267167%

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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