Abstract
This paper aims to verify the capability of fuzzy inference system in establishing time series model for ship manoeuvrability. The traditional modeling approaches are usually based on a unified framework. Due to the presence of outliers or noises in ship sailing records, it is difficult in achieving satisfactory performance directly from data. In this paper, we propose a combined time series modeling method by the use of data mining technique and fuzzy system theory. Data mining concepts are introduced to improve the fuzzy rule extraction algorithm to make the resulting fuzzy inference system more robust with respect to the noises or outliers. A ship 20°/20° zig-zag test is simulated. The data point records in time series are obtained from an actual manoeuvring test. With comprehensive robustness analysis, our fuzzy inference system using data mining technology is proved to be a robust and accurate tool for ship manoeuvring simulation.
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Bai, Y., Li, T., Gao, X. (2013). Ship Maneuvering Modeling Based on Fuzzy Rules Extraction and Optimization. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39068-5_52
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DOI: https://doi.org/10.1007/978-3-642-39068-5_52
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