causal system


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causal system

[′kȯ·zəl ‚sis·təm]
(control systems)
A system whose response to an input does not depend on values of the input at later times. Also known as nonanticipatory system; physical system.
References in periodicals archive ?
A system is said to be causal system if its output depends on present and past inputs only.
Liao, "Design of optimal output regulators for multirate linear discrete-time descriptor causal system with time delay," Journal of University of Science and Technology Beijing, vol.
Given this implication, if participants are indeed sensitive to the context, then presumably DDM performance should suffer if the underlying causal system contradicts the context that the system is couched in.
The asymptotic limits are derived via Kramers-Kronig relations, which relate the real and imaginary parts of a linear causal system response, or equivalently its phase and magnitude response.
A causal system depends on and is determined by past and current conditions-- either locally or non-locally.
The bulk of Pearl's book deals with inference problems where we have only partial information about the causal system being modeled.
System performance measures provide information (statistical) for taking action on the causal system to improve future performance from the customer's perspective.
It cannot be direct, because the only direct evidence concerning what any causal system will do when it executes a program results from program testing.
116 acknowledges], moreover is that the behavior displayed by a causal system is an effect of the complete set of relevant factors whose presence or absence made a difference to its production.
This approach is based on the interpretation of mental disorders as causal systems.
She considers Anselm's notion of moral responsibility to be similar to that of modern defenders of libertarianism: a sort of rational intervention into the causal course of events through a special category of agent-causation that stands apart from all other causal systems.
Building on recently established connections between dynamical systems and causal models, CAFES will develop theory and algorithms for causal modeling, reasoning, discovery and prediction for cyclic causal systems.