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Nonlinear processes are difficult to control because there can be so many variations of the nonlinear behavior. Traditionally, a nonlinear process has to be linearized first before an automatic controller can be effectively applied. This is typically achieved by adding a reverse nonlinear function to compensate for the nonlinear behavior so the overall proces input-output relationship becomes somewhat linear. The adaptive systems are best handled with methods of computational intelligence such as neural networks and fuzzy systems. This presentation will focus on severa! methods of developing close to optima! architectures and on finding efficient learning algorithms. The problem becomes even more complex if the methods of computational intelligence have to be implemented in hardware. V arious practical solutions will be presented and compared.
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Operational Program Digital Poland, 2014-2020, Measure 2.3: Digital accessibility and usefulness of public sector information; funds from the European Regional Development Fund and national co-financing from the state budget.
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Click the link below to view the content.https://www.ibspan.waw.pl/~alex/OZwRCIN/WA777_0_KS-2005-03-R14P03_XV Krajowa Konferencja Automatyki : Warszawa, 27-30 czerwca 2005. t. 3 * Sztuczna inteligencja * Implementation of methods of computational intelligence (referat problemowy)_content.pdf