基于改进聚类分析的神经网络集成方法应用研究

随着工业生产规模扩大、生产过程日趋复杂,人们对过程模拟提出了更高的要求。提出一种基于改进聚类分析的神经网络集成方法。首先,根据数据密度分布,改进传统K-均值聚类分析中初始中心点选取的不足,对样本进行分类,扩大样本间的差异性;第二,运用具有快速学习能力的广义回归神经网络算法对各类样本建立个体神经网络并进行训练;第三,对所有样本增加构造补偿神经网络,进行误差补偿,以消除由于选择错误造成的输出误差;最后,根据计算得到的聚类中心对输入样本进行数值分析,选择输出个体神经网络,并与构造的补偿网络输出进行比较,最终实现神经网络集成。通过人工数据Sinc验证模型,此方法有效提高了模型精度,对提高过程模拟准确性提供了新途径。

Application research of neural network ensemble method based on improved clustering analysis

With the expansion of industrial production scale and the increasing complexity of production process,demands  for process simulation are increasing. In the paper, we propose an improved clustering analysis based neural network ensemble method.Firstly,according to the density distribution of data,an improvement in K means method is made to overcome the disadvantage in the selection of the original central point，and then the samples are classified and the differences among the samples are enlarged.Secondly,aiming to the different samples, the general regression neural network (GRNN) with fast learning ability is used to construct and train individual neural networks.Thirdly,a compensation network is constructed for all the samples by GRNN so as to eliminate the output errors due to false selection.Finally,the obtained clustering center is utilized to make numerical analysis for the input samples and to select the individual neural network.The output of the selected individual neural network is compared with the output of the compensation network,and the neural network ensemble is realized.The verification on artificial data Sinc illustrates that the model accuracy is enhanced by the proposed method.Thus,it provides a new way for increasing the accuracy of process simulation.