CAJ | 학술논문

根据人工神经网络自组织、高度并行以及具有非线性映射能力的特点，提出一种基于云计算的Hadoop多模式并行分类算法。通过将自组织映射网络与多个并行BP神经网络结合，提高多语义模式中复杂分类问题的学习效率和训练精度。采用Hadoop平台下的MapReduce框架实现算法的并行处理，解决大规模数据样本训练时内存开销大、通信耗时长的问题。实验结果表明，与传统单BP多输出分类算法相比，该算法训练速度更快、分类精度更高，在处理大规模数据集时具有实时和高效的特性。
근거인공신경망락자조직、고도병행이급구유비선성영사능력적특점，제출일충기우운계산적Hadoop다모식병행분류산법。통과장자조직영사망락여다개병행BP신경망락결합，제고다어의모식중복잡분류문제적학습효솔화훈련정도。채용Hadoop평태하적MapReduce광가실현산법적병행처리，해결대규모수거양본훈련시내존개소대、통신모시장적문제。실험결과표명，여전통단BP다수출분류산법상비，해산법훈련속도경쾌、분류정도경고，재처리대규모수거집시구유실시화고효적특성。
Based on Back Propagation Neural Network(BPNN) characteristics of self-organized,highly parallel and nonlinear mapping capabilities,this paper presents a multi-mode parallel Self-organizing Mapping Multi-back Propagation Neural Network ( SOM-MBP ) classification algorithm under Hadoop. It combinies Self-organizing Mapping ( SOM ) network and BP neural networks to increase the learning efficiency and training accuracy of complex multi-mode parallel classification problems,and uses MapReduce framework on Hadoop to implement parallel processing in order to solve large memory overhead and communication time-consuming problems which are caused by large-scale data training. Experimental results indicate that the algorithm achieves a faster training speed and higher classification accuracy than traditional single BP multi-output classification algorithm. The parallel algorithm exhibits characteristics of real-time and high efficiency in dealing with large-scale data set.