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dc.contributor.authorYousif Al Mashhadany-
dc.date.accessioned2022-10-20T06:18:45Z-
dc.date.available2022-10-20T06:18:45Z-
dc.date.issued2010-
dc.identifier.issn2706-7440-
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/3682-
dc.description.abstractThe feasibility of using an Artificial Neural Network (ANN) for controlling timevarying dynamical system is presented. The direct adjusting of neural controller by direct adaptive control (DAC) is available, by using the error between output of plant and desired input. The finite recurrent back propagation (FRBP) is used in the learning process, because the ability of this method to capture the nonlinearly and overcome the problem of time varying system. Hybrid controller structure used in this paper, where the parameters of classical controller are adjusted with time at specified freezing points for time varying dynamical system, and summed the outputs of two controllers and enter to the plant, identify of system by ANN to get the optimal initial condition for neuro controller. A single channel for Spacecraft model is used as an example in this paper, satisfactory results are obtained, which explain the ability of recurrent neural network (RNN) to identify time varying dynamical system and overcome for all its problem and explain the ability of this structure of hybrid neuro controller to use with time varying dynamical system.en_US
dc.language.isoenen_US
dc.publisherAnbar Journal for Engineering Sciencesen_US
dc.subjectfinite recurrent backpropagation network (FRBN)en_US
dc.subjecttime varying systemen_US
dc.subjectdirect neural model reference adaptive system (DNMRAS)en_US
dc.titleNeuro Controller For Time Varying Dynamical Systemen_US
dc.typeArticleen_US
Appears in Collections:الهندسة الكهربائية

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