Application and Research of the Granular Evolutionary Neural Network Algorithms for the Complex Network

Yong Qin Tao; Ping Ding Zhang; Qing Li

doi:10.4028/www.scientific.net/AMM.321-324.2080

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 321-324Application and Research of the Granular...

Application and Research of the Granular Evolutionary Neural Network Algorithms for the Complex Network

Abstract:

Research on an "Granular Evolutionary Neural Network Algorithms (GENNA)" is applied to the complex network. The theory of the granular quotient space is introduced to the neural network. At first input variables of the neural network are granulated to equivalence classes, so that the input variables of the network structure can be simplified, and have certain clustering characteristics and strong diversity, and then the network parameters and the weights are optimized using evolutionary algorithms, so as to avoid neural network to fall into the local extremum.The experimental results show that the algorithm effectively narrow the search space and accelerate the speed of convergence , and It is feasibility and effectiveness.

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Periodical:

Applied Mechanics and Materials (Volumes 321-324)

Pages:

2080-2084

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.321-324.2080

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Online since:

June 2013

Authors:

Yong Qin Tao, Ping Ding Zhang, Qing Li

Keywords:

Complex Network, Genetic Algorithm (GA), Granular Computing, Neural Network (NN)

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References

[1] Hao Shuyan,Ma Cuiling.Construction of complex network and its reliability[J].IEEE, P 2008- 2011 (2011)

DOI: 10.1109/aimsec.2011.6011044

Google Scholar

[2] MA Kai,HE Zhi-qin.Modeling and Optimization of Complex Objects Based on Genetic Algorithm and Neural Network.Modular[J]achine Tool & Automatic Manufacturing Technique, 32-25,no1(2012)

Google Scholar

[3] LIU Fa-sheng,LUO Yan-rong.Community structure discovery in complex networks based on multi-population genetic algorithm[J].Application Research of Computers,29(4),pp.1237-1240(2012)

Google Scholar

[4] Wang yang, Evolving Models and Topology Optimization on Complex Network[D].Dong Nan niversity paper,(2009)

Google Scholar

[5] Xue Fuqiang,Ge Lindong,Wang Bin.Optimised neural network channel equalized based on improved hierarchical genertic algorithm[J].Computer Applications and Softwar, 27(5), pp.75-88 (2010)

Google Scholar