Subsystems {control subsystem} {system controls} at same level inhibit each other. Higher subsystems compare lower-subsystem outputs over time and space, to send summaries to even higher subsystems.
Inputs can control system performance {control problem}. Control can keep output near reference value {regulator problem}. Control can follow trajectories {tracking problem}. Good control methods use independent positive and negative signals with wavelength and amplitude ranges. Such control signals have close control, smooth response, and good sensitivity.
Paths {express line} {control express line}, from low-level to high-level nodes, suggest, find, activate, prime, or inhibit hypotheses, models, or patterns. Paths, from high-level to low-level nodes, find, activate, prime, or inhibit lower-level nodes or indexes, for searches.
Some output {feedback} can be input to regulate output [Wiener, 1947]. Feedback can compensate for minor departures from output level.
loop
Feedback loop continuously measures output {indicator, control}, modifies input {executive organ, control}, connects indicator and executive organ {transmitter, control}, and supplies energy {motor}. System parts {feedback mechanism} can subtract actual from intended output and send more or less signal to decrease differences, using algorithms {identification algorithm}.
setting
Feedback refines behavior but does not set behavior level, which is set manually.
signal
Too-great signals {overcompensation} cause cycles, as output overshoots intended output. Too-small signals {undercompensation} are not enough to overcome noise or inaccuracies and so fail to return system to expected performance.
Classification algorithms can use prototypes or templates {feedforward}. Classification results when stimulus parts closely match prototype or template parts.
feedforward mechanism
Input can regulate output {feedforward mechanism}, by sending signals based on system states and environment to enhance or initiate actions. Feedforward sets output level based on algorithm or system model. After sending feedforward signal, system sends no more signals for a time {refractory period, feedforward}, to allow time to check first-signal results.
examples
Feedforward classification algorithms include feature-based winner-take-all algorithms {Pandemonium algorithm}, feedforward neural nets using feedback during learning {backpropagation, feedforward}, tree-based classifiers, and parametric statistical modeling [Selfridge, 1970].
Mechanisms {homeostasis, system} can use feedback controls.
Feedback {negative feedback} can dampen responses to maintain goal level. Negative-feedback algorithms can use different comparator types. ON-OFF regulation uses a constant set point. Proportional regulation uses variable set points. For constant disturbances, integral regulation uses constant set points, but output change rate is proportional to input. Derivative regulation uses input-change rate, proportional to output [Kampis, 1991].
Feedback {positive feedback} can synchronize events and deliver maximum response quickly, good for behavior rituals.
3-Computer Science-System Analysis
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Date Modified: 2022.0225