首页> 外文期刊>Sensor Letters: A Journal Dedicated to all Aspects of Sensors in Science, Engineering, and Medicine >Prediction of Aquaculture Water Quality Based on Combining Principal Component Analysis and Least Square Support Vector Regression
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Prediction of Aquaculture Water Quality Based on Combining Principal Component Analysis and Least Square Support Vector Regression

机译:基于主成分分析和最小二乘支持向量回归的水产养殖水质预测

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摘要

The traditional methods about the water quality prediction in the aquaculture can't resolve the data redundancy and complex characters, which makes the accuracy of forecast precision low. In order to improve the accuracy of model prediction and the response time, this study demonstrates a new prediction method based on combing principal component analysis and least square support vector regression (PCA-LSSVR). The principal component analysis (PCA) method is applied to dimension reduction which reduces redundant data between various factors and extracting the principal components, then the data pre-processing is finished. The least square support vector regression (LSSVR) method is for modelling and forecasting to the principal components. Comparative experiment of the water quality is conducted on the base of river crab aquaculture ponds in Yixing. The experimental results show that the model of PCA-LSSVR outperforms standard LSSVR in performance on average, and the dimension of the sample set not only can be reduced and the water quality prediction accuracy can also be improved effectively. The research is proved to have stronger practical value.
机译:传统的水产养殖水质预测方法无法解决数据冗余和特征复杂的问题,从而降低了预测精度的准确性。为了提高模型预测的准确性和响应时间,本研究演示了一种基于主成分分析和最小二乘支持向量回归(PCA-LSSVR)相结合的新预测方法。将主成分分析(PCA)方法用于降维,以减少各种因素之间的冗余数据并提取主成分,然后完成数据预处理。最小二乘支持向量回归(LSSVR)方法用于对主要成分进行建模和预测。在宜兴市河蟹养殖池塘的基础上进行了水质对比试验。实验结果表明,PCA-LSSVR模型的平均性能优于标准LSSVR,不仅可以减小样本集的大小,而且可以有效提高水质预测的准确性。实践证明,该研究具有较强的实用价值。

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