PurposeThe purpose of this paper is to identify the Nomoto ship model parameters accurately, in order to produce a very close match between the predictions based on the model and the full‐scale trials.Design/methodology/approachVarious ship maneuvering mathematical models have been used when describing the ship dynamics behavior. The Nomoto ship model is a class of simplified hydrodynamic derivative type models which are the most widely used, accepted and perhaps well developed. To determine the model parameters accurately, particle swarm optimization (PSO) is chosen as an evolution algorithm in this paper. This arithmetic can guarantee the convergence and global optimization ability, and avoid sinking into a local optimal solution.FindingsThe process of PSO for identifying the Nomoto ship model parameters is given.Research limitations/implicationsAvailability of the full‐scale trial data are the main limitations.Practical implicationsThe ship model parameters provide very useful advice in ship's autopilot process.Originality/valueThe paper presents a new parameter identification method for the second‐order Nomoto ship model based on PSO.

This paper generalises the GM(1,1) direct modeling method with a step by step majorizing grey derivative's whiten values to unequal time interval sequence modeling, and proves that the new method still has linear transformation consistency of the old method. The example indicates that the new method still has gradual approaching white exponential law coincidence property. With this new method, we then model the high precision soft foundation settlement.

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It is an effective approach to combine state signal processing with fuzzy mathematics for recognizing fuzziness and randomness clearly. This paper shows how the fuzzy membership functions are obtained from the result of signal processing of fault diagnosis in electrical equipment. So the properties of electrical equipment are revealed more thoroughly. Finally, the effectiveness of the method was proved by an example.

Based on ideal absolute errors and relative errors, a new grey model‐GMp(1,1) model is presented. The existence problem of its solution is also solved based on a few conditions. Then, MGMp(1,n) model is presented. These optimized models GMp(1,n) and MGMp(1,n) have good anti‐noise property to absolute and relative errors. Examples illustrate that they have very good fitting and forecasting results.