计算机科学 ›› 2015, Vol. 42 ›› Issue (11): 288-292.doi: 10.11896/j.issn.1002-137X.2015.11.059
陈珍,夏靖波,柏骏,徐敏
CHEN Zhen, XIA Jing-bo, BAI Jun and XU Min
摘要： 信息全面与维数灾难的矛盾是大数据时代网络态势感知需要解决的首要难题。特征提取一直是主流的降维方法,但现有算法对高维非线性数据效果不佳；深度学习是一类具有多层非线性映射的学习算法,可以完成复杂函数的逼近,但对隐层相关参数十分敏感。针对上述问题,将进化算法的思想引入深度学习,提出了一种基于进化深度学习的特征提取算法。该算法利用遗传算法及进化策略实现全局搜索及优化的特点,并对深度学习结构及相关参数进行了优化。理论分析及实验结果都证明了该算法的有效性。
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