结合自适应遗传算法与弹性BP神经网络的亚硝酸盐预测模型  

Nitrite prediction model based on adaptive genetic algorithm and elastic BP neural network

作　　者：林志贵[1,2,3] 姚芳琴 冯林强[1] 杜军兰[1] 李建雄[3] 

机构地区：[1]国家海洋技术中心近海海洋环境观测与监测技术研究室,天津300112 [2]天津大学精密仪器与光电子工程学院,天津300072 [3]天津工业大学电子与信息工程学院,天津300387

出　　处：《天津工业大学学报》2015年第3期67-72,共6页Journal of Tiangong University

基　　金：国家自然科学基金项目资助(61372011)

摘　　要：针对目前营养盐检测主要是通过化学方法实现，无法获得在线检测的问题，利用营养盐与其影响因子之间的关系，提出结合自适应遗传算法与弹性BP神经网络的预测模型。利用改进的自适应遗传算法，通过交叉、变异获取弹性BP神经网络的初始权值与阈值，加速预测过程。该模型通过营养盐影响因子数据，预测亚硝酸盐浓度。仿真结果表明：基于弹性BP神经网络的预测模型预测营养盐浓度是可行的，其预测得到的亚硝酸盐浓度值的相对误差主要集中于0-30%；结合自适应遗传算法与弹性BP神经网络的预测模型的预测效果好于基于弹性BP神经网络的预测模型。Currently nutrients are detected by the chemical method. A chemical method cannot get online detection. To solve the problem, based on the relationship between nutrients and their impact factors, a prediction model which combined Adaptive Genetic Algorithm and Elastic BP Neural Network is put forward in this paper. Using the improved Adaptive Genetic Algorithm, the initial weights and thresholds of Elastic BP Neural Network are obtained by the crossover and mutation to accelerate the prediction process. The imporoved model predicts the nitrite by using the data of its impact factors. Simulation results show that it is feasible to predict the nutrient concentration by using the prediction model based on the Elastic BP Neural Network. The relative error of nitrite concentration value mainly focuses on 0-30%. The prediction model based on Adaptive Genetic Algorithm and Elastic BP neural network is better than that based on Elastic BP Neural Network.

关 键 词：亚硝酸盐 预测 自适应遗传算法 弹性BP神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]