In control theory a self-tuning system is capable of optimizing its own internal running parameters in order to maximize or minimize the fulfillment of an objective function; typically efficiency or error.

Self-tuning systems typically exhibit non-linear adaptive control. Self-tuning systems have been a hallmark of the aerospace industry for decades, as this sort of feedback is necessary to generate optimal multivariable control for nonlinear processes. In the telecommunications industry, adaptive communications are often used to dynamically modify operational system parameters to maximize efficiency and robustness.


Examples of self-tuning systems in computing include:

Performance benefits can be substantial. Professor Jack Dongarra, American computer scientist, claims self-tuning boosts performance often on the order of 300%.

Digital Self-tuning Controllers are an example of self-tuning systems at the hardware level.


Self-tuning systems are typically composed of four components: expectations, measurement, analysis, and actions. The expectations describe how the system should behave given exogenous conditions.

Measurements gather data about the conditions and behavior. Analysis helps determine whether the expectations are being met- and which subsequent actions should be performed. Common actions are gathering more data and performing dynamic reconfiguration of the system.


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