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Case study : Comparison between statics and dynamics neural network model of nonlinear system identification

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dc.contributor.affiliationen_US
dc.contributor.authorAkmal Asyraf Bin Tiken_US
dc.date.accessioned2023-05-03T15:34:20Z
dc.date.available2023-05-03T15:34:20Z
dc.date.issued2008
dc.description.abstractThis project is predominantly research based project. Literature review of various types and characteristics of nonlinear system and nonlinear system identification was done. Neural network was chosen as the method for system identification. After studying the characteristics of the nonlinear system, a nonlinear model was chosen based on the mathematical equation of the system. A Simulink model of the system was constructed based on the mathematical expression of the nonlinear model. The purpose of building this model is to collect the data in order to analyze the model later. Since the output of nonlinear system is differ for every simulation, it is important to have the same input and output data pair for every analysis.en_US
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/20916
dc.language.isoenen_US
dc.subjectSystem identificationen_US
dc.subjectNeural network (Computer science)en_US
dc.titleCase study : Comparison between statics and dynamics neural network model of nonlinear system identificationen_US
dc.typeResource Types::text::Thesisen_US
dspace.entity.typePublication
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