Study on Improved Flexible Neural Tree Optimization Algorithm

Abstract:

The BP neural network is easy to fall into local minimum point, the algorithm convergence speed slow, this paper puts forward an improved algorithm of flexible neural tree, introduced the basic theory knowledge of Flexible neural tree , analyzes the characteristics and advantages of the neural tree. The structure optimization and parameter optimization are adopted some optimization algorithm, Introduced the multi expression programming algorithm for optimization of flexible neural tree structure and by using the improved particle swarm algorithm to optimize the parameters of flexible neural tree, Finally the establishment of complete flexible neural tree model.

• Periodical: Advanced Materials Research (Volumes 765-767)
• Pages: 1055-1059
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.765-767.1055
• Online since: September 2013
• Authors: Yu Wang
• Keywords: Fitness, Flexible Neural Tree, Multi Expression Programming Optimization, Parameter Optimization

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