基于ARMA-BP神经网络组合模型的藻类生物量和半径预测

Shigang Cui, Y. Liu, Yongli Zhang, Lin He, Xingli Wu
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引用次数: 1

摘要

虾青素是一种高效的天然抗氧化剂,广泛应用于水产养殖、化妆品、医药等领域。雨红球菌富含虾青素,是虾青素提取的主要来源[1]。监测雨红球菌的生长状况是提取虾青素的前提条件。半径是反映藻类细胞生长状态的重要指标,对藻类生物量和半径的检测具有重要意义[2]。本文的重点是雨红球菌的增殖和培养阶段。选取三个影响因素作为输入,建立BP神经网络模型。建立的BP模型对ARMA模型中的非线性残差进行估计,整合整个序列的预测结果,最终建立ARMA-BP预测模型。效果为ARMA-BP模型>BP神经网络模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Algae biomass and radius prediction based on ARMA-BP neural network combination model
Natural astaxanthin is a highly effective antioxidant widely used in aquaculture, cosmetics and medicine. Haematococcus pluvialis is rich in astaxanthin, which is the main source of astaxanthin extraction [1]. Monitoring the growth status of Haematococcus pluvialis is a prerequisite for extracting astaxanthin. Radius is an important indicator of the growth state of algae cells, which is of great significance for the detection of algal biomass and radius [2]. The focus of this paper is on the proliferation and culture stage of Haematococcus pluvialis. The BP neural network model was established by selecting three influencing factors as inputs. The established BP model estimates the nonlinear residuals in the ARMA model, integrates the prediction results of the entire sequence, and finally establishes the ARMA-BP prediction model. The effect is ARMA-BP model >BP neural network model.
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