Variable Structure Neural Network Based on Improved Estimation of Distribution Algorithm

Author

Zhang Yi ; Wu Jinhua ; Yang Xiuxia

Author_Institution

Dept. of Control Eng., Naval Aeronaut. & Astronaut. Univ., Yantai, China

fYear

2009

fDate

11-13 Dec. 2009

Firstpage

1

Lastpage

4

Abstract

Combining with genetic algorithm, the improved estimation of distribution algorithm (EDA) is provided. The crossover and mutation operations are added and the "elite" individuals are retained, which can keep the excellent evolution mode. The selection based on energy entropy is added, which can explore the solution space sufficiently and keep the population diversity. A neural network with switches introduced to its links is proposed. The method of tuning the structure and parameters of the neural network using the improved EDA is provided. The carrying robot inverse dynamics model approximation example show the validity of this algorithm.

Keywords

distributed algorithms; entropy; estimation theory; neural nets; crossover operations; distribution algorithm estimation; energy entropy; excellent evolution mode; mutation operations; population diversity; robot inverse dynamics model approximation; switches; variable structure neural network; Diversity reception; Electronic design automation and methodology; Entropy; Genetic algorithms; Genetic mutations; Inverse problems; Neural networks; Orbital robotics; Space exploration; Switches;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-4507-3

Electronic_ISBN

978-1-4244-4507-3

Type

conf

DOI

10.1109/CISE.2009.5362541

Filename

5362541