Intelligent control 741104 211020856 2008-05-08T12:59:17Z SmackBot 433328 Date the maintenance tags or general fixes '''intelligent control''' is a class of [[control]] techniques, that use various AI computing approaches like [[neural networks]], [[Bayesian probability]], [[fuzzy logic]], [[machine learning]], [[evolutionary computation]] and [[genetic algorithm]]s. == Overview == Intelligent control can be devided into the following major sub-domains: * [[Neural network]] control * [[Bayesian probability|Bayesian]] control * [[fuzzy logic|Fuzzy]] (logic) control * [[Neuro-fuzzy]] control * [[Expert System]]s * Genetic control * [[Intelligent agent]]s (Cognitive/Conscious control) New control techniques are created continuously as new models of intelligent behavior are created and computational methods developed to support them. === Neural network controllers === [[Neural networks]] have been used to solve problems in almost all spheres of science and technology. Neural network control basically involves two steps: * System identification * Control It has been shown that a [[feedforward]] network with nonlinear, continuous and differentiable activation functions have [[universal approximation capability]]. [[Recurrent]] networks have also been used for system identification. Given, a set of input-output data pairs, system identification aims to form a mapping among these data pairs. Such a network is supposed to capture the dynamics of a system. === Bayesian controllers === [[Bayesian probability]] has produced a number of algorithms that are in common use in many advanced control systems, serving as [[State space (controls)|state space]] [[estimator]]s of some variables that are used in the controller. The [[Kalman filter]] and the [[Particle filter]] are two examples of popular Bayesian control components. The Bayesian approach to controller design requires often an important effort in deriving the so-called system model and measurement model, which are the mathematical relationships linking the state variables to the sensor measurements available in the controlled system. In this respect, it is very closely linked to the [[Systems theory|system-theoretic approach]] to [[Control engineering|control design]]. == See also == *[[action selection]] *[[artificial intelligence]] *[[Function approximation]] == References == {{Unreferenced|date=May 2008}} == Further reading == * Jeffrey T. Spooner, Manfredi Maggiore, Raul Ord onez, and Kevin M. Passino, ''Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximator Techniques'', John Wiley & Sons, NY ; * Jay Farrell, Marios Polycarpou, ''Adaptive Approximation Based Control:Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches'', John Wiley & Sons, NJ [[Category:Control theory]] [[Category:Artificial intelligence]]