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Extended immune programming and oppositebased pso for evolving flexible beta basis function neural tree.
| Content Provider | CiteSeerX |
|---|---|
| Author | Bouaziz, Souhir Alimi, Adel M. Abraham, Ajith |
| Abstract | Abstract — In this paper, a new hybrid learning algorithm based on the global optimization techniques, is introduced to evolve the Flexible Beta Basis Function Neural Tree (FBBFNT). The structure is developed using the Extended Immune Programming (EIP) and the Beta parameters and connected weights are optimized using the Opposite-based Particle Swarm Optimization (OPSO) algorithm. The performance of the proposed method is evaluated for time series prediction area and is compared with those of associated methods. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Immune Programming Extended Immune Programming Time Series Prediction Area Global Optimization Technique Associated Method Opposite-based Particle Swarm Optimization Beta Parameter |
| Content Type | Text |
