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Modeling Complex Systems by Generalized Factor Analysis
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DOI：10.1109/TAC.2014.2357913.png)
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Abstract
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We propose a new modeling paradigm for large dimensional aggregates of stochastic systems by Generalized Factor Analysis (GFA) models. These models describe the data as the sum of a flocking plus an uncorrelated idiosyncratic component. The flocking component describes a sort of collective orderly motion which admits a much simpler mathematical description than the whole ensemble while the idiosyncratic component describes weakly correlated noise. We first discuss static GFA representations and characterize in a rigorous way the properties of the two components. The extraction of the dynamic flocking component is discussed for time-stationary linear systems and for a simple classes of separable random fields.
Keywords:
Collective behavior
complex systems
flocking
generalized factor analysis
multi-agent systems
stochastic systems
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