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Charles Roberto Telles

Publications and source records attributed to Charles Roberto Telles.

3 recordsLinked to original sources

False Asymptotic Instability Behavior at Iterated Functions with Lyapunov Stability in Nonlinear Time Series

Empirically defining some constant probabilistic orbits of f(x) and g(x) iterated high-order functions, the stability of these functions in possible entangled interaction dynamics of the environment through its orbit's connectivity (open sets) provides the formation of an exponential dynamic fixed point b = S(n+1) as a metric space (topological property) between both iterated functions for short time lengths. However, the presence of a dynamic fixed point f(g(x)) (x+1) can identify a convergence at iterations for larger time lengths of b (asymptotic stability in Lyapunov sense). Qualitative (QDE) results show that the average distance between the discontinuous function g(x) to the fixed point of the continuous function f(x) (for all possible solutions), might express fluctuations of g(x) on time lengths (instability effect). This feature can reveal the false empirical asymptotic instability behavior between the given domains f and g due to time lengths observation and empirical constraints within a well-defined Lyapunov stability.

nlin.CD

Work sharing as a metric and productivity indicator for administrative workflows

Defining administrative workflow events as a nonlinear dynamics that assume a random ordered or disordered growth rate of information processing, a method has been proposed for large-scale administrative systems that structures hybrid system variables (continuous or discrete) as iterated and attracted to a fixed-point event at which for all possible metric spaces solutions, the modeling of variables from Lyapunov exponential stability point of view allows the projection of system performance to be oriented, that is, the relationship between the number of agents and the number of administrative services within an administrative workflow environment.

nlin.AO

Productivity equation and the m distributions of information processing in workflows

This research investigates an equation of productivity for workflows regarding its robustness towards the definition of workflows as probabilistic distributions. The equation was formulated across its derivations through a theoretical framework about information theory, probabilities and complex adaptive systems. By defining the productivity equation for organism-object interactions, workflows mathematical derivations can be predicted and monitored without strict empirical methods and allows workflow flexibility for organism-object environments.

math.OC