摘要
将主成分分析(PCA)与模糊反向传播(BP)网络建模方法相融合,提出了PCA-模糊BP方法并用于藻类繁殖状态的预测,建立了叶绿素a含量的预测模型.采用PCA对各类采集数据进行预处理,并将PCA所得各理化因子作为模糊BP网络的输入变量,叶绿素a的含量作为模糊BP网络的输出变量,经过学习训练,获得藻类繁殖状态的预测模型.结果表明,PCA-模糊BP方法降低了各类输入样本数据之间的相关性和模型系统的维数,加快了模糊BP网络的收敛速度,其与典型BP神经网络模型相比,具有更快的计算速度和更高的预测精度,能够较好地预测海洋藻类繁殖的生长状况.
Principal component analysis （PCA） method combined with fuzzy back propagation （BP） network, called as PCA-fuzzy BP method, was proposed to predict the state of algae reproduction, and the prediction model of chlorophyll-a concentration was established. PCA method was used to preprocess various acquisition data, it reduces the dimensionality of the input data of the system. The physical-chemical factors produced by PCA processing can be regarded as the input variables of the fuzzy BP network, the concentration of chlorophyll-a can be regarded as the output of fuzzy BP network, and the state prediction model of algae reproduction can be obtained by learning and training for this network. The experimental result indicates that PCA method reduces the correlation for the factors of input sample data, also reduces the dimension of the model system, accelerates the speed of modelling convergence for fuzzy BP network. The PCA-fuzzy BP algorithm has a faster speed of calculation and more accuracy of prediction than the typ- ical BP network. This kind of model can give a better state prediction for algae reproduction.
出处
《上海交通大学学报》
EI
CAS
CSCD
北大核心
2012年第5期785-789,共5页
Journal of Shanghai Jiaotong University
基金
上海海事大学科研基金项目资助(20110036)
关键词
主成分分析
模糊BP网络模型
叶绿素A
状态预测
principal component analysis （PCA）
fuzzy back propagation （BP） network modelehlorophyll-a
state prediction
