Abstract
In this work, some ubiquitous neural networks are applied to model the landscape of a known problem function approximation. The performance of the various neural networks is analyzed and validated via some well-known benchmark problems as target functions, such as Sphere, Rastrigin, and Griewank functions. The experimental results show that among the three neural networks tested, Radial Basis Function (RBF) neural network is superior in terms of speed and accuracy for function approximation in comparison with Back Propagation (BP) and Generalized Regression Neural Network (GRNN).
Original language | English |
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Pages (from-to) | 586-594 |
Number of pages | 9 |
Journal | Procedia Computer Science |
Volume | 17 |
DOIs | |
Publication status | Published - 2013 |
Event | 1st International Conference on Information Technology and Quantitative Management, ITQM 2013 - Suzhou, China Duration: 16 May 2013 → 18 May 2013 |
Keywords
- Benchmark function
- Function approximation
- Neural network