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Qualitative Analysis of Chaos in Nonlinear Sampled Data Systems and SystemIdentification

机译:非线性采样数据系统中混沌的定性分析与系统识别

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Analysis of nonlinear sampled data systems is considered. A qualitative approachis adopted in which the system is suspended in a parameterized cell state space framework. Application of a global unravelling algorithm reveals the type of behavior typically found using dynamical systems techniques. Steady state, periodic, and aperiodic or chaotic behavior is detected. System identification techniques are then applied. The resulting nonlinear recursive input output model is analyzed within the above framework and is shown to exhibit the same periodic characteristics as the original nonlinear sampled data system.

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