Pairan, Mohammad Fahmi and Shamsudin, Syariful Syafiq and Mohd Anwar, Mohd Shazlan (2021) Real-time system identification of an unmanned quadcopter system using fully tuned radial basis function neural networks. Int. J. Modelling, Identification and Control, 37 (2). pp. 128-139.
![[img]](http://eprints.uthm.edu.my/style/images/fileicons/text.png) |
Text
J13806_75a91a97938fdff9b81357997c6950b6.pdf Restricted to Registered users only Download (1MB) | Request a copy |
Abstract
In this paper, we present the performance analysis of a fully tuned neural network trained with the extended minimal resource allocating network (EMRAN) algorithm for real-time identification of a quadcopter. Radial basis function network (RBF) based on system identification can be utilised as an alternative technique for quadcopter modelling. To prevent the neurons and network parameters selection dilemma during trial and error approach, RBF with EMRAN training algorithm is proposed. This automatic tuning algorithm will implement the network growing and pruning method to add or eliminate neurons in the RBF. The EMRAN’s performance is compared with the minimal resource allocating network (MRAN) training for 1000 input-output pair untrained attitude data. The findings show that the EMRAN method generates a faster mean training time of roughly 4.16 ms for neuron size of up to 88 units compared to MRAN at 5.89 ms with a slight reduction in prediction accuracy.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Quadcopter; system identification; neural network; fully tuned neural network; RBF; radial basis function. |
| Subjects: | Q Science > QA Mathematics > QA801-939 Analytic mechanics |
| Divisions: | Faculty of Mechanical and Manufacturing Engineering > Department of Aeronautical Engineering |
| Depositing User: | Mr. Abdul Rahim Mat Radzuan |
| Date Deposited: | 07 Apr 2022 02:21 |
| Last Modified: | 07 Apr 2022 02:21 |
| URI: | http://eprints.uthm.edu.my/id/eprint/6881 |
Actions (login required)
 |
View Item |