ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) FOR FRICTION MODELLING AND COMPENSATION IN MOTION CONTROL SYSTEM

2011 ◽  
Vol 31 (1) ◽  
Author(s):  
Ismaila B. Tijani ◽  
Martono Wahyudi ◽  
Hashim Talib
Keyword(s):  
Control System ◽  
Motion Control ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Motion Control System ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Friction Modelling

scholarly journals Design an intelligent hybrid position/force control for above knee prosthesis based on adaptive neuro-fuzzy inference system

2021 ◽  
Vol 23 (2) ◽  
pp. 675
Author(s):  
Mithaq N. Raheema ◽  
Dhirgaam A. Kadhim ◽  
Jabbar S. Hussein
Keyword(s):  
Control System ◽  
Knee Joint ◽  
Force Control ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Knee Prosthesis ◽  
Current Control ◽  
Inference System ◽  
Control Approach ◽  
Neuro Fuzzy

<div>This paper reviews the position/force control approach for governs an efficient knee joint in an active lower limb prosthesis, and the inter facing current control algorithm with human gate parameter is inserted. Two techniques are used to collect gait cycle data of leg: first, the foot ground force is obtained by the force platform device based on its position (x, y), then data of knee joint angles is recorded by using a video-camera device.The collected information is sent and used in the proposed intelligent controller. This intelligent control system used an adaptive neuro-fuzzy inference system (ANFIS) circuit in addition to the proportional integral derivative (PID) controller. This hybrid ANFIS-PID control system simulates and provides the ground force values. The experimental results show anexcellent response and lower root mean square error (RMSE) compared with each of PID and ANFIS controller that implemented for a similar purpose. In summary, the results showed acceptably stable performance of the proposedposition/force controller based on hybrid ANFIS-PID system. It can be concluded that the finest performance of the controlled force, as quantified by the RMSE criteria, is perceived by the proposed hybrid scheme depending on the controller intelligent decision circuit.</div>

An Wavelet Based Handoff Algorithm for Cognitive Wireless Network

2012 ◽  
Vol 433-440 ◽  
pp. 5087-5091
Author(s):  
Meng Jia Li ◽  
Jing Yao Wang ◽  
Mei Song ◽  
Xiao Jun Wang ◽  
Ning Ning Liu
Keyword(s):  
Control System ◽  
Wavelet Analysis ◽  
Fuzzy Control ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Second Use

This paper proposed a novel handoff algorithm for cognitive network based on wavelet analysis and fuzzy control system. It makes the system cognitive and adaptive to the changes of the environment by two steps: first, make wavelet analysis to the received signal to get the basic signal which is without noise. Second, use adaptive neuro-fuzzy inference system (ANFIS) to make diligent handoff decision. The simulation shows that it improves the performance of the whole system when the channel is in low signal-to-noise ratio.

scholarly journals Basement Flood Control with Adaptive Neuro Fuzzy Inference System Using Ultrasonic Sensor

2020 ◽  
Vol 5 (2) ◽  
pp. 11
Author(s):  
Raden Muhamad Yuda Pradana Kusumah ◽  
Maman Abdurohman ◽  
Aji Gautama Putrada
Keyword(s):  
Control System ◽  
Water Level ◽  
Fuzzy Inference System ◽  
Flood Control ◽  
Fuzzy Inference ◽  
Flood Management ◽  
Ultrasonic Sensor ◽  
Training Data ◽  
Inference System ◽  
Neuro Fuzzy

This paper proposes a basement flood management system based on Adaptive Neuro Fuzzy Inference System (ANFIS). Basement is one of the main parts of a building that has a high potential for flooding. Therefore, the existence of a flood control system in the basement can be a solution to this threat. Water level control is the key to solving the problem. Fuzzy Inference System (FIS) has proven to be a reliable method in the control system but this method has limitations, that is, it needs to have a basis or a reference when determining the fuzzy set. When there is no basis or reference, Adaptive Neuro FIS (ANFIS) can be a solution. The Neuron aspect in ANFIS determines fuzzy sets through training data. In terms of the Internet of Things (IoT), this system uses an ultrasonic sensor, Node Red IoT platform, and Matlab Server.  Then the water pump will turn on to control the water level when there is rainfall. By undergoing a comparative test with the FIS method, ANFIS provides a lower Root Mean Square Error (RMSE) and is recommended for use in basement flood management systems.

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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