A Hybrid Metaheuristic Embedded System for Intelligent Vehicles Using Hypermutated Firefly Algorithm Optimized Radial Basis Function Neural Network
| Autor: | Hsu-Chih Huang, Shao-Kang Lin |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
business.industry Computer science 020208 electrical & electronic engineering 02 engineering and technology Computer Science Applications Control and Systems Engineering Control theory Embedded system 0202 electrical engineering electronic engineering information engineering Firefly algorithm Electrical and Electronic Engineering business Metaheuristic Information Systems |
| Zdroj: | IEEE Transactions on Industrial Informatics. 15:1062-1069 |
| ISSN: | 1941-0050 1551-3203 |
| DOI: | 10.1109/tii.2018.2796556 |
| Popis: | This paper presents a hybrid metaheuristic embedded system for intelligent vehicles using hypermutated firefly algorithm (FA)-optimized radial basis function neural network (RBFNN), called FA-RBFNN. With the Mecanum vehicle's dynamic model, the FA with hypermutation is fused with RBFNN to develop a real-time optimal controller of the four-wheeled Mecanum vehicles in a field-programmable gate array (FPGA) chip. This hybrid metaheuristics takes the benefits of neural network, FA, real-time control, and FPGA realization. All the FA-RBFNN, dynamic controller, and hardware circuits are implemented in one FPGA chip using System-on-a-Programmable Chip methodology. Comparative works and experimental results clearly illustrate that the proposed FPGA-based FA-RBFNN optimal controller outperforms the conventional control methods. |
| Databáze: | OpenAIRE |
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