PREDICTION OF DYNAMIC DEFORMATION BASED ON CHAOTIC
IMMUNE OPTIMIZATION ALGORITHM BRF NEURAL NETWORK
Zhu Yu; Zhao Qing; and Mei Yan
School of Geodesy and Geomatics, Jiangsu Normal University, Xuzhou 221116
Abstract Aiming to the shortcoming of the traditional prediction model, a method for designing the RBF neural network based on chaotic immune optimization algorithm (CIOA) is proposed, which uses CIOA to the RBF network center vector and weights optimization. By applying chaos mutation operator to producing new antibody and applying immune selection operator to realizing the survival of the fittest, CIOA is able to maintain a good diversity. At the same time, CIOA has higher convergence speed and it can effectively avoid falling into local optima. The results show that chaotic immune optimization RBF neural network applied to the prediction of dynamic deformation, effectively improve the predicted speed and performance.
Key words ：
chaotic immune optimization algorithm(CIOA)
RBF neural network model
dynamic deformation monitoring
optimization model
prediction
Received: 01 January 1900
Corresponding Authors:
Zhu Yu
Cite this article:
Zhu Yu,Zhao Qing,and Mei Yan. PREDICTION OF DYNAMIC DEFORMATION BASED ON CHAOTIC
IMMUNE OPTIMIZATION ALGORITHM BRF NEURAL NETWORK[J]. , 2012, 32(5): 53-57.
Zhu Yu,Zhao Qing,and Mei Yan. PREDICTION OF DYNAMIC DEFORMATION BASED ON CHAOTIC
IMMUNE OPTIMIZATION ALGORITHM BRF NEURAL NETWORK[J]. jgg, 2012, 32(5): 53-57.
URL:
http://www.jgg09.com/EN/ OR http://www.jgg09.com/EN/Y2012/V32/I5/53
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