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Prediction Research of Red Tide Based on Improved FCM

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  • Xiaomei Hu
  • Dong Wang
  • Hewei Qu
  • Xinran Shi

Abstract

Red tides are caused by the combination effects of many marine elements. The complexity of the marine ecosystem makes it hard to find the relationship between marine elements and red tides. The algorithm of fuzzy -means (FCM) can get clear classification of things and expresses the fuzzy state among different things. Therefore, a prediction algorithm of red tide based on improved FCM is proposed. In order to overcome the defect of FCM which is overdependent on the initial cluster centers and the objective function, this paper gains the initial cluster centers through the principle of regional minimum data density and the minimum mean distance. The feature weighted cluster center is added to the objective function. Finally, the improved FCM algorithm is applied in the prediction research of red tide, and the results show that the improved FCM algorithm has good denoising ability and high accuracy in the prediction of red tides.

Suggested Citation

  • Xiaomei Hu & Dong Wang & Hewei Qu & Xinran Shi, 2016. "Prediction Research of Red Tide Based on Improved FCM," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-8, January.
    • Handle: RePEc:hin:jnlmpe:9618706
    DOI: 10.1155/2016/9618706
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    Cited by:

    1. Tingting Wang & Zhuolin Li & Xiulin Geng & Baogang Jin & Lingyu Xu, 2022. "Time Series Prediction of Sea Surface Temperature Based on an Adaptive Graph Learning Neural Model," Future Internet, MDPI, vol. 14(6), pages 1-13, May.

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