Abstract
In order to ensure the safe operation of offshore platform, we need response to the platform motion and forecast mooring force. The prediction method based on numerical calculation and model experiment, has certain limitation. A new principle and method of ship’s mooring load measurements based on indirect measurement is presented in order to achieve the short-term and high-precision mooring load prediction, and an algorithm is proposed through which predictions are made by comb the wavelet multi-scale decomposition and reconstruction method with BP neural networks. This paper, by putting a prototype data as learning samples, using the neural network algorithm for forecasting of mooring force, overcomes the traditional B P neural network faults, gets a higher precision. Through comparing the measured data, it demonstrates the feasibility of this method in engineering application.
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