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- Title
Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer.
- Authors
Rani R., Hannah Jessie; Victoire T., Aruldoss Albert
- Abstract
This paper presents an integrated hybrid optimization algorithm for training the radial basis function neural network (RBF NN). Training of neural networks is still a challenging exercise in machine learning domain. Traditional training algorithms in general suffer and trap in local optima and lead to premature convergence, which makes them ineffective when applied for datasets with diverse features. Training algorithms based on evolutionary computations are becoming popular due to their robust nature in overcoming the drawbacks of the traditional algorithms. Accordingly, this paper proposes a hybrid training procedure with differential search (DS) algorithm functionally integrated with the particle swarm optimization (PSO). To surmount the local trapping of the search procedure, a new population initialization scheme is proposed using Logistic chaotic sequence, which enhances the population diversity and aid the search capability. To demonstrate the effectiveness of the proposed RBF hybrid training algorithm, experimental analysis on publicly available 7 benchmark datasets are performed. Subsequently, experiments were conducted on a practical application case for wind speed prediction to expound the superiority of the proposed RBF training algorithm in terms of prediction accuracy.
- Subjects
WIND speed; RADIAL basis functions; PARTICLE swarm optimization; EVOLUTIONARY computation; ALGORITHMS
- Publication
PLoS ONE, 2018, Vol 13, Issue 5, p1
- ISSN
1932-6203
- Publication type
Article
- DOI
10.1371/journal.pone.0196871