Abstract
The minimization method is widely used to predict the dynamic characteristics of a system. Generally, data recorded by experiment (for example displacement) tends to contain noise, and the error in the properties of the system is proportional to the noise level (NL). In addition, the accuracy of the results depends on various factors such as the signal character, filtering method or cut off frequency. In particular, coupled terms in multimode systems show larger differences compared to the true value when measured in an environment with a high NL. The iterative least square (ILS) method was proposed to reduce these errors that occur under a high NL, and has been verified in previous research. However, the ILS method might be sensitive to the signal processing, including the determination of cutoff frequency. This paper focused on improving the accuracy of the ILS method, and proposed the modified ILS (MILS) method, which differs from the ILS method by the addition of a new calculation process based on correlation coefficients for each degree of freedom. Comparing the results of these systems with those of a numerical simulation revealed that both ILS and the proposed MILS method provided good prediction of the dynamic properties of the system under investigation (in this case, the damping ratio and damped frequency). Moreover, the proposed MILS method provided even better prediction results for the coupling terms of stiffness and damping coefficient matrix.