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Identification of Computational Models under Dynamic Loads
This technical publication presents the results of developing and implementing effective methods for identifying dynamic systems. It covers models for both deterministic and stochastic objects, analyzing core identification techniques. The book examines the application of time-domain, frequency-domain, stochastic, parametric, and non-parametric methods, all of which have been practically applied in real-world object research.
Key topics include the formulation and solution of identification problems for linear and nonlinear objects under conditions of a priori uncertainty regarding statistical characteristics, using robust algorithms. The work demonstrates the feasibility of numerically implementing these identification methods and procedures. The text is supported by numerous examples that illustrate the technology behind the procedures for solving identification tasks.
Intended for researchers and engineers in design organizations, this book is also recommended for undergraduate, master’s, and postgraduate students in civil engineering programs studying applied and experimental mechanics.