DC Field | Value | Language |
---|---|---|
dc.contributor.author | SONG, JEONG JUN | - |
dc.contributor.author | PARK, SUNWON | - |
dc.date.accessioned | 2011-09-22T08:27:14Z | - |
dc.date.available | 2011-09-22T08:27:14Z | - |
dc.date.issued | 1993 | - |
dc.identifier.citation | Journal of Chemical Engineering of Japan, Vol.26 , No.4, pp.347-354 | en |
dc.identifier.issn | 0021-9592 | - |
dc.identifier.uri | http://hdl.handle.net/10203/25275 | - |
dc.description.abstract | A neural model predictive contrl strategy combining a neural network for plant identification and a nonlinear programming algorithm for solving nonlinear control problems is proposed. A constrainted nonlinear optimization approach using successive quadratic programming combined with a neural identification network is used to generate the optimum control law for complex continuous chemical reactor systems that have inherent nonlinear dynamics. The neural model predictive controller(NMPC) shows good performance and robustness. | en |
dc.language.iso | en_US | en |
dc.publisher | The Society of Chemical Engineers | en |
dc.subject | Neural Network | en |
dc.subject | Model Predictive Control | en |
dc.subject | Identification | en |
dc.subject | Intelligent Control | en |
dc.subject | Nonlinear Control | en |
dc.title | Neural Model Predictive Control for Nonlinear Chemical Processes | en |
dc.type | Article | en |
dc.identifier.doi | 10.1252/jcej.26.347 | - |
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