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An actual problem of identification theory is considered related to the non-formalized task of evaluating the model structure. Novel approaches to structural identification (SI) propose solutions to various problems of identification theory based on the analysis of geometric frameworks (GFs). This formalized approach to the structural identifiability (SID) for nonlinear dynamical systems of various classes shows that structural identifiability follows from SI. Additionally, based on the GF, estimates for the Lyapunov exponents (LEs) of dynamical systems are shown to be recoverable, detectable, and identifiable. When combined with synthesized methods and algorithms, they can be applied to the construction of mathematical models for complex processes and systems. Thus, they can be used in decision-making systems, process forecasting, control of nonlinear systems, and processing of heterogeneous time series. Novel Approaches to Structural Identification Using Geometric Framework Analysis proposes various solutions to the problem of identification theory. It discusses the development of adaptive identification and control systems for analyzing complex processes and systems. Covering topics such as parametric restrictions, distributed lags, and interconnected systems, this book is an excellent resource for data analysis specialists, mathematical software developers, professionals, researchers, scholars, academicians, and more.

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