SVM kernel based on particle swarm optimized vector and Bayesian optimized SVM in atmospheric particulate matter forecasting
SVM kernel based on particle swarm optimized vector and Bayesian optimized SVM in atmospheric particulate matter forecasting
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DOI：
10.1016/j.asoc.2020.106410
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发表时间：
2020-08-01
影响因子：
8.7
通讯作者：
Kouziokas, Georgios N.
中科院分区：
文献类型：
--
作者：
Kouziokas, Georgios N.
近年来，随着人工智能新技术和新工艺的发展，人工智能在许多科学领域的应用得到了提升。考虑到在文献中有一个非常有限的数量的研究，提出和测试新的SVM核回归问题，本研究介绍了一种新的SVM核，通过将一个转换的粒子群优化的神经网络权重向量的贝叶斯优化的SVM核的时间序列问题预测大气污染因子颗粒物10（PM10）。该模型引入了一个新的支持向量机内核，说明了增加的预测精度相比，传统的优化人工神经网络和支持向量机模型，根据实验结果。所提出的方法的研究结果表明，新提出的支持向量机核可以被用来作为一种改进的预测技术。(c)2020爱思唯尔B.V.保留所有权利。
The application of Artificial Intelligence (AI) has been upgraded in many scientific fields the last years, with the development of new artificial intelligence-based technologies and techniques. Considering that in the literature there is a very limited number of studies proposing and testing new SVM kernels in regression problems, this research introduces a novel SVM Kernel by incorporating a transformed particle swarm optimized ANN weight vector in a Bayesian optimized SVM kernel in a time series problem for predicting the atmospheric pollutant factor Particulate Matter 10 (PM10). The proposed model introduces a new SVM kernel that illustrates an increased forecasting accuracy compared to the conventional optimized ANN and SVM models according to the experimental results. The findings of the proposed methodology illustrate that the new proposed SVM Kernel can be utilized as an improved forecasting technique. (c) 2020 Elsevier B.V. All rights reserved.