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As per available reports about 8 Conferences, 18 Relevant Journals, 3 Workshops and 3 Conference Proceedings are presently dedicated exclusively to Intelligent Control and 8 articles are dedicated to Intelligent Control.
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 algorithms.
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Scope and Importance:
Intelligent control achieves automation via the emulation of biological intelligence. It either seeks to replace a human who performs a control task or it borrows ideas from how biological systems solve problems and applies them to the solution of control problems. New control techniques are created continuously as new models of intelligent behavior are created and computational methods developed to support them. Neural networks have been used to solve problems in almost all spheres of science and technology. 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.
BCC Research divides the global market for smart machines into five segments: expert systems, autonomous robots, digital assistants, embedded systems and neurocomputers. The global market for these systems is expected to grow to $15.3 billion by 2019 with an average annual growth rate of 19.7 percent. Expert systems (e.g. medical decision support systems and smart grids) made up the largest share of the market in 2013 followed by autonomous robots. BCC Research expects the autonomous robot category to account for the largest share — 22.8 percent — of annual market growth until 2024 and thus to dominate the smart machine market.
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This page was last updated on 15th Sep, 2015
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