Nawawi, Zainudin and Suprapto, Bhakti Yudho (2022) Corresponding author : Neural network training for serial multisensor of autonomous vehicle system. IAES.
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Abstract
This study aims to find the best artificial neural network weight values to be applied to the autonomous vehicle system with ultrasonic multisensor. The implementation of neural network in the system required long time process due to its training process. Therefore, this research is using offline training before implementing to online training by embedding the best network weight values to obtain the outputs faster according to desired targets. Simulink were used to train the system offline. Eight ultrasonic sensors are used on all sides of the vehicle and arranged in a serial multisensory configuration as inputs of neural network. With eight inputs, one sixteen-depth hidden layer, and five outputs, it was trained using the back-propagation algorithm of artificial neural network. By 100000 iterations, the output values and the target values are almost the same, indicating its convergency with minimum of errors. The result of this training is the best weights of the networks. These weight values can be implemented as fixed-weight in online training
| Item Type: | Other |
|---|---|
| Subjects: | #3 Repository of Lecturer Academic Credit Systems (TPAK) > Corresponding Author |
| Divisions: | 03-Faculty of Engineering > 20201-Electrical Engineering (S1) |
| Depositing User: | Mr. Bhakti Suprapto |
| Date Deposited: | 29 Apr 2023 23:44 |
| Last Modified: | 29 Apr 2023 23:44 |
| URI: | http://repository.unsri.ac.id/id/eprint/98326 |
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