A New Algorithm for Detection of Animal and Plant Ion Concentration Based on Gene Expression Programming

Pub Date : 2023-02-16 DOI:10.4018/ijcini.318144
Kangshun Li, Leqing Lin, Jiaming Li, Siwei Chen, H. Jalil
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Abstract

In order to accurately predict the concentration detection data of ion sensors for animal and plant, this paper proposes a gene expression programming (GEP) based concentration detection method. The method includes collecting ion concentration data as well as voltage timing data; preprocessing all the collected data to obtain an initial sample set; constructing a prediction model of ion concentration, which is an explicit functional relationship between voltage and the concentration of a specific ion. The Gene Expression Programming is used to train and evaluate the prediction model, and obtain a trained model. By comparing gene expression programming with other two modeling methods, it is found that the accuracy of the model established by gene expression programming has greater advantages than that established by polynomial fitting and neural network in processing animal and plant ion concentration data.
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基于基因表达编程的动植物离子浓度检测新算法
为了准确预测动植物离子传感器的浓度检测数据,本文提出了一种基于基因表达编程的浓度检测方法。该方法包括收集离子浓度数据以及电压定时数据;对所有收集的数据进行预处理以获得初始样本集;构建离子浓度预测模型,该模型是电压与特定离子浓度之间的显式函数关系。基因表达编程用于训练和评估预测模型,并获得训练后的模型。通过将基因表达编程与其他两种建模方法进行比较,发现在处理动植物离子浓度数据时,基因表达编程建立的模型的准确性比多项式拟合和神经网络建立的模型具有更大的优势。
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