Identification and Control of Nonlinear Systems Using PID and ANN - Shebani, Amer
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The aims of this work are the identification and control of non-linear systems. Investigation of control of non-linear systems using PID controller presented. An alternative artificial neural network to overcome the limitations associated with the backpropagation algorithm such as slow convergence and construction complexity is the radial basis function neural network (RBFNN) which has been widely used for solving many complex problems. The RBFNN used on this work to control of nonlinear systems.

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Produktbeschreibung
The aims of this work are the identification and control of non-linear systems. Investigation of control of non-linear systems using PID controller presented. An alternative artificial neural network to overcome the limitations associated with the backpropagation algorithm such as slow convergence and construction complexity is the radial basis function neural network (RBFNN) which has been widely used for solving many complex problems. The RBFNN used on this work to control of nonlinear systems.
Autorenporträt
Shebani, Amer
Dr. Amer Shebani has obtained his PhD degree in control engineering in 2017. He worked in power plant as instrumentation and control engineer, and then, he worked as a lecturer on higher education. He is the author of several articles published in reputed journals.