Syafitri, Niken and Adang Suwandi, Ahmad and Mutijarsa, Kusprasapta (2008) Neuro-Fuzzy Based Obstacle Avoidance for Autonomous Vehicle. In: UNSPECIFIED.
|
Text
2008 - conference - IES.pdf Download (280kB) | Preview |
Abstract
The research developed neuro-fuzzy based obstacle avoidance for autonomous vehicle. The method consists of fuzzy system that was equipped with supervised learning and reinforcement learning. Fuzzy system with supervised learning was divided to three method, fuzzy system with Delta Rule (DR), fuzzy system with General Delta Rule (GDR) and fuzzy system with General Delta Rule with Fuzzy Parameter Adaptation (GDRFPA). In DR, three simulations were done to this method. First, simulation was with no boundary value. Second was with boundary value. Third was with parameters that close to the needed output values. In GDRFPA, fuzzy parameter adaptation for learning rate and momentum constant were used. All of method compared to know what the fastest and accurate method in learning
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Divisions: | Karya Tulis Ilmiah |
| Depositing User: | Asep Kamaludin |
| Date Deposited: | 16 Jan 2020 09:20 |
| Last Modified: | 16 Jan 2020 09:20 |
| URI: | http://eprints.itenas.ac.id/id/eprint/603 |
Actions (login required)
 |
View Item |