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中文摘要：
提出一种基于文化算法的选择性神经网络集成方法．该方法通过文化算法选择部分网络来组成神经网络集成，并将多层信念空间引入文化算法框架，充分利用了优秀个体所包含的种群信息，使个体间保持较大的差异度，减少“多维共线性”和样本噪声的影响．实验表明，该方法能够提高神经网络集成的性能．
英文摘要：
To improve the ability of generalization, a selective constructing approach to neural network ensemble is proposed,in which the culture algorithm is used to select part of the trained individual networks to be ensembled. This method puts multilayer belief spaces into the framework of culture algorithm which can fully utilize the outstanding characteristics of the individual that may maintain the diversity of neural networks and decrease the effect of collinearity and noise of sample. The experimental results prove that culture algorithm can effectively improve the efficiency to select diversity individual neural networks to construct ensemble.
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