中文摘要：

针对动态变形数据常规预测模型的不足,提出了一种基于混沌免疫优化的RBF模型,即利用混沌免疫算法（CIOA）对RBF网络的中心向量及连接权值进行优化。CIOA结合了免疫算法和混沌优化算法各自的空间搜索优势,在免疫进化的过程中引入混沌寻优方法,改善算法的寻优模式,提高算法的收敛速度,避免算法陷入局部最优。结果表明：将混沌免疫优化RBF网络应用于动态变形数据预测中,有效地提高了预测的速度和性能。

英文摘要：

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.