中文摘要：

能够实现线性可分并易分的特征集对模式分类具有重要意义，而特征计算方法的有限性和吲定性往往导致构建此类特征集存在一定困难。为此提出一种基于神经网络和主元分析（Principal component analysis，PCA）的特征集构建方法，该方法利用神经网络对已有特征集进行非线性映射生成新的特征集，继而利用PCA方法对新特征集进行降维处理，在满足信息保留率大于85％的条件下只取第一主元方向投影数据，并判断线性可分和易分性。设计在第一主元方向上判断新特征集是否满足线性可分和易分的判据算法和准则，给出利用不对称交叉遗传算法进行网络寻优的具体步骤。数值仿真和试验验证表明所提出的方法性能稳定、分类准确，而且泛化能力较强，具有一定的工程应用价值。

英文摘要：

The feature set, which can make the classification linear and simple, has important meaning to pattern classification. But it is always difficult to generate such feature set because of the limitation and fixity of the calculation methods of feature set. A novel method generating above-mentioned feature set is proposed, which is based on the neural network and principal component analysis（PCA）. In this method, the neural network is used to carry out nonlinear mapping of the existing feature set, thereby generating a new feature set, and then PCA is used to reduce the dimension of the new feature set. On first principal component direction, if the information reservation rate is greater than 85%, the performance of linear and simple classification of the projection data will be evaluated. Then the evaluation method is designed, and a training approach of neural network using a novel GA algorithm whose crossover operation is asymmetric is proposed. The numerical simulation and experiments show that the performance of the novel method is stable, the classification is accurate and the generalization capability is remarkable.