Neural network training with parallel particle swarm optimizer
- Author:
Zheng QIN
1
Author Information
1. Department of Computer Science and Technology
- Publication Type:Journal Article
- From:Academic Journal of Xi'an Jiaotong University
2006;18(2):109-112
- CountryChina
- Language:Chinese
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Abstract:
Objective: To reduce the execution time of neural network training. Methods: Parallel particle swarm optimization algorithm based on master-slave model is proposed to train radial basis function neural networks, which is implemented on a cluster using MPI libraries for inter-process communication. Results: High speed-up factor is achieved and execution time is reduced greatly. On the other hand, the resulting neural network has good classification accuracy not only on training sets but also on test sets. Conclusion: Since the fitness evaluation is intensive, parallel particle swarm optimization shows great advantages to speed up neural network training.
