A comparison of optimization algorithms for biological neural network identification
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Article number | 5173522 |
| Pages (from-to) | 1127-1131 |
| Journal / Publication | IEEE Transactions on Industrial Electronics |
| Volume | 57 |
| Issue number | 3 |
| Publication status | Published - Mar 2010 |
Link(s)
Abstract
Recently, the identification of biological neural networks has been reformulated as an optimization problem based on a framework of adaptive synchronization. In this paper, four different optimization algorithms, including genetic algorithm, jumping gene genetic algorithm (JGGA), tabu search, and simulated annealing, have been applied for this optimization problem. Based on the simulation results, their performances are compared, and it is concluded that JGGA can outperform the other three methods in term of minimizing the synchronization and parameter estimation errors. © 2006 IEEE.
Research Area(s)
- Biological neural network (BNN), Genetic algorithms (GAs), Identification, Optimization methods
Citation Format(s)
A comparison of optimization algorithms for biological neural network identification. / Yin, J. J.; Tang, Wallace K. S.; Man, K. F.
In: IEEE Transactions on Industrial Electronics, Vol. 57, No. 3, 5173522, 03.2010, p. 1127-1131.
In: IEEE Transactions on Industrial Electronics, Vol. 57, No. 3, 5173522, 03.2010, p. 1127-1131.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
