This paper presents a global optimization method of radial basis function networks. In the proposed method, stochastic search by simulated annealing is combined with a local search technique in order to perform global optimization of the network parameters with enhanced convergence speed. Its convergence property is proved mathematically. Experimental results demonstrate that the proposed method improves the performance of the networks over the conventional local and global training methods and reduces influence of the initial parameter values on the final results.
|Number of pages||19|
|Journal||Neural Network World|
|Publication status||Published - 2010 Sep 22|
All Science Journal Classification (ASJC) codes
- Hardware and Architecture
- Artificial Intelligence