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Improved parameter identification algorithm for ship model based on nonlinear innovation decorated by sigmoid function
Transportation Safety and Environment
This paper investigates the problem of parameter identification for ship nonlinear Nomoto model with small test data, a nonlinear innovation-based identification algorithm is presented by embedding sigmoid function in the stochastic gradient algorithm. To demonstrate the validity of the algorithm, an identification test is carried out on the ship 'SWAN' with only 26 sets of test data. Furthermore, the identification effects of the least squares algorithm, original stochastic gradient algorithmdoi:10.1093/tse/tdab006 fatcat:kgxleiuwzbedpkjrwiw7efcwt4