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| Main Authors: | , |
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| Format: | Recurso digital |
| Language: | |
| Published: |
Zenodo
2015
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| Subjects: | |
| Online Access: | https://doi.org/10.5281/zenodo.14009000 |
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Table of Contents:
- <div> <div> <div> <div> <div> <p>Radial Basis Function Neural Networks (RBFNNs) have been widely used for classification and function approximation tasks.‎ Hence, it is worthy to try improving and developing new learning algorithms for RBFNNs in order to get better results.‎ This paper presents a new learning method for RBFNNs.‎ An improved algorithm for center adjustment of RBFNNs and a novel algorithm for width determination have been proposed to optimize the efficiency of the Optimum Steepest Decent (OSD) algorithm.‎ To initialize the radial basis function units more accurately, a modified approach based on Particle Swarm Optimization (PSO) is presented.‎ The obtained results show fast convergence speed, better and same network response in fewer train data which states the generalization power of the improved neural network.‎ The Improved PSO–OSD and Three-phased PSO–OSD algorithms have been tested on five benchmark problems and the results have been compared.‎ Finally, using the improved radial basis function neural network we propose a new method for object image retrieval.‎ The images to be retrieved are object images that can be divided into foreground and background.‎ Experimental results show that the proposed method is really promising and achieves high performance.‎</p> </div> </div> </div> </div> </div>