Abstract
This book chapter proposes a new training algorithms for Radial Basis Function (RBF) using a recently proposed optimization algorithm called Moth-Flame Optimizer (MFO). After formulating MFO as RBFN trainer, seven standard binary classifications are employed as case studies. The MFO-based trainer is compared with Particle Swarm Algorithm (PSO), Genetic Algorithm (GA), Bat Algorithm (BA), and newrb. The results show that the proposed trainer is able to show superior results on the majority of case studies. The observation of convergence behavior proves that this new trainer benefits from accelerating convergence speed as well.
| Original language | English |
|---|---|
| Title of host publication | Handbook of Neural Computation |
| Publisher | Elsevier Inc. |
| Pages | 537-550 |
| Number of pages | 14 |
| ISBN (Electronic) | 9780128113196 |
| ISBN (Print) | 9780128113189 |
| DOIs | |
| Publication status | Published - 1 Jan 2017 |
| Externally published | Yes |
Keywords
- Metaheuristics
- MFO
- Moth Flame Optimizer
- Neural Networks
- Optimization
- Radial Basis Function
- RBF
- Training
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Cite this
Faris, H., Aljarah, I., & Mirjalili, S. (2017). Evolving Radial Basis Function Networks Using Moth-Flame Optimizer. In Handbook of Neural Computation (pp. 537-550). Elsevier Inc.. https://doi.org/10.1016/B978-0-12-811318-9.00028-4