Abstract: When combining grey system with RBF neural network local optimization and convergence problems are still existed，so genetic algorithm is introduced to assist the modeling of grey neural network in this paper.Genetic algorithm is employed to solve the parameters of improved GM（1，1） with Lagrange’s Mean Value Theorem，two new dynamic prediction models integrating genetic algorithm and grey RBF，one is a grey RBF compensation prediction GA-GRBF model，the other is inlaid grey neural network GRBF model.The new models with preferable structure and parameters are applied to simulation and analysis of time-displacement data of wind response.The comparative experiment results show that this model is capable of predicting a small sample of data accurately，easily and conveniently.

Key words: GM（1, 1）, Radial Basis Function（RBF）, genetic algorithm based grey RBF prediction model, GA-GRBF model, optimization, errors compensation

摘要： 针对灰色系统结合RBF神经网络时算法存在局部最优和收敛性等问题，引入遗传算法来辅助优化灰色神经网络预测模型。利用具有的较强全局搜索能力，且收敛速度快的遗传算法对GM（1，1）模型参数λ进行高效求解，然后融合RBF神经网络和改进的灰色GM（1，1）模型，构成两种不同结构的基于遗传算法的灰色RBF预测模型，一种是灰色RBF补偿预测模型GA-GRBF，另一种是灰色嵌入型GRBF模型。以某智能监控系统采集的风响应时程数据进行仿真分析，结果表明经过遗传算法优化的GRBF模型都要优于单一的GRBF模型，并且GA-GRBF模型建模简单，预测精度高，实用性强。

关键词: GM（1, 1）模型, 径向基函数, 基于遗传算法的灰色RBF预测模型, GA-GRBF模型, 优化, 残差补偿, ,