A Coyote Optimization Algorithm-trained Dendritic Neuron Model for Prediction Tasks

Houtian He, Chenxi Xue, Qianqian Li, Shangce Gao

研究成果: 書籍の章/レポート/会議録会議への寄与査読

1 被引用数 (Scopus)

抄録

Time series prediction is always a hot topic. Many artificial neural network-based models are continuously proposed to improve the predicting performance. However, in practical situations, the large scale of training data and the complex structure of network often lead to slow training speed and unsatisfactory result. Thus, it is reasonable to use a suitable algorithm to train a well-structured network for such an issue. In this paper, we use the coyote optimization algorithm to train a dendritic neuron and propose the COADNM to tackle time series prediction problems. Three datasets are used to verify the performance of the proposed model, meanwhile, four other neural network-based models are employed to compare with COADNM. The experiment result shows that our model outperforms all its competitors via different evaluation criteria.

本文言語英語
ホスト出版物のタイトル2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2022
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781665499248
DOI
出版ステータス出版済み - 2022
イベントJoint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2022 - Ise, 日本
継続期間: 2022/11/292022/12/02

出版物シリーズ

名前2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2022

学会

学会Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2022
国/地域日本
CityIse
Period2022/11/292022/12/02

ASJC Scopus 主題領域

  • 人工知能
  • コンピュータ サイエンスの応用
  • 制御と最適化
  • モデリングとシミュレーション
  • 数値解析

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