Identification of climate types in natural environment based on PSO-SVM

Wei Zhang; Ning Li; Keyu Yi; Min Han; Jian Ma; Junfeng Duan

doi:10.1117/12.2652581

10 November 2022 Identification of climate types in natural environment based on PSO-SVM

Wei Zhang, Ning Li, Keyu Yi, Min Han, Jian Ma, Junfeng Duan

Proceedings Volume 12331, International Conference on Mechanisms and Robotics (ICMAR 2022); 123312S (2022) https://doi.org/10.1117/12.2652581
Event: International Conference on Mechanisms and Robotics (ICMAR 2022), 2022, Zhuhai, China

Abstract

The operation of electrical measuring equipment is affected by the local climate. Classifying the climate of each place based on environmental parameters is an important guide to the design and manufacture of electrical measurement equipment. Therefore, a support vector machine classification prediction model based on kernel parameter optimization is proposed. Firstly, linear discriminant analysis is used to extract the features of various environmental parameters. Then, support vector machine classification model is used to train the extracted environmental feature data. Finally, in order to improve the prediction accuracy of the model, particle swarm optimization algorithm was used to optimize the kernel parameters of the model to achieve the classification prediction of the model. The model is verified based on climate data, and the experimental results show that the use of the proposed model can effectively realize the classification and recognition of climate data.

Citation Download Citation

Wei Zhang, Ning Li, Keyu Yi, Min Han, Jian Ma, and Junfeng Duan "Identification of climate types in natural environment based on PSO-SVM", Proc. SPIE 12331, International Conference on Mechanisms and Robotics (ICMAR 2022), 123312S (10 November 2022); https://doi.org/10.1117/12.2652581

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