Nonlinear system identification based on neural networks with locally distributed dynamics and application to technical processes:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | Undetermined |
| Veröffentlicht: |
Düsseldorf
VDI-Verl.
1996
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| Ausgabe: | Als Ms. gedr. |
| Schriftenreihe: | Verein Deutscher Ingenieure: [Fortschritt-Berichte VDI / 8]
591 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Zugl.: Darmstadt, Techn. Hochsch., Diss., 1996 |
| Beschreibung: | X, 158 S. Ill., graph. Darst. |
| ISBN: | 3183591081 |
Internformat
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Datensatz im Suchindex
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| adam_text | Titel: Nonlinear system identification based on neural networks with locally distributed dynamics and appli
Autor: Ayoubi, Mihiar
Jahr: 1996
FORTSCHRITTBERICHTE VW Dipl.-Ing. Mihiar Ayoubi, Darmstadt Nonlinear System Identification Based on Neural Networks with Locally Distributed Dynamics and Application to Technical Processes Reihe 8 : Meß-, Steuerungsund Regelungstechnik Nr. 591 VW VERLAG
Contents Page V Contents Abbreviations and Nomenclature 1. Introduction .......................................................1 1.1 System Analysis: Modelling and Identification......................I 1.2 Identification based on Neural Networks, State of the Art .............2 1.2.1 Networks with Lumped Dynamics..........................3 1.2.2 Recurrent Neural Networks...............................7 . 1.2.3 Networks with Locally Distributed Dynamics.................7 1.2.4 Posing the Problem .....................................9 2. Overview of Neural Structures ........................................11 2.1 Definition Categories of Neural Networks .......................11 2.2 Generalized Neuron Model .....................................16 2.2.1 Input Operator...........................................16 2.2.2 Activity Function ........................................17 2.2.3 Learning Algorithm.......................................18 3. Static Mapping Neural Networks ......................................21 3.1 Multi-Layer Perceptron Network ................................21 3.1.1 Single Perceptron and Delta-Rule........................21 3.1.2 Generalized Delta-Rule, Error Backpropagation...............24 3.1.3 Function Approximation with MLP-Networks ................27 3.2 Radial Basis Function Network .................................29 3.2.1 Function Approximation with RBF-Networks ................29 3.2.2 RBF Networks versus MLP Networks.......................31 4. Dynamic Mapping Neural Networks ...................................34 4.1 Spatiotemporal Mapping .......................................34 4.2 Multi-Layer Perceptron with Distributed Dynamics ..................36 4.2.1 Dynamic Perceptron Model...............................36 4.2.2 Identification Capability of the Dynamic Perceptron ............40 4.2.3 Cost Function and Error Surface...........................43 4.2.4 Dynamic Delta-Rule and
Parameter-Sensitivity Filter...........46 4.2.5 Relationship to Nonlinear Regression Stochastic Approximation ................................49 4.2.6 Gradient Filtering (Momentum Effect )......................51 4.2.7 Parameter-Sensitivity Initialization.........................52 4.2.8 Generalized Dynamic Delta-Rule ..........................53 4.2.9 Summary .............................................59
Page VI Contente 4.3 Radial Basis Functions with Distributed Dynamics .................60 4.3.1 Dynamic Radial Unit....................................61 4.3.2 Dynamic Adaptation Algorithm............................62 4.3.3 Dynamic Radial Basis Function Network ....................63 4.3.4 Reticulating the Radial Units .............................65 4.3.5 Summary .............................................66 5. Compound Estimation Procedure ......................................67 5.1 CEP- Adaptation of Filter Coefficients within DMLP-Networks ........68 5.2 CEP-Adaptation of Filter Coefficients within DRBF-Networks.........71 5.2.1 Uniform Eigen-behaviour -Approach ......................71 5.2.2 Ratio of Static Gains -Approach ..........................72 6. Simulation Results on Nonlinear Dynamic System Identification..............75 6.1 Unique Nonlinearity Involved in System s Input.....................75 6.1.1 Identification Based on the DMLP .........................76 6.1.2 Identification Based on the DRBF .........................^ 79 6.1.3 Identification Based on the Hammerstein Model .............. 80 6.2 Unique Nonlinearity Involved in System s Output ................... 6.2.1 Identification Based on the DMLP .........................** 82 6.2.2 Identification Based on the Wiener Model ................... 83 6.3 System with Nonlinear Feedback................................. 84 6.3.1 Identification Based on the DMLP ......................... 6.3.2 Identification Based on the DRBF .........................^ 6.4 Dynamic Nonlinearity Identification Based on the DMLP ............. 6.5 Summary ................................................... 90 7. Experimental Results on Nonlinear, Dynamic System Identification........... 7.1 Application to the Identification of a Heat Exchanger................. 7.1.1 Outline Description of the Thermal Plant .................... 7.1.2 Model Identification Based on
DMLP....................... .96 7.1.3 Dynamic Model Validation ............................ ^ 7.1.4 Approximated Steady State Characteristic .................. ^ 7.1.5 Summary ............................................ ^ 7.2 Application to a Turbocharged Diesel Engine .................... ^ 7.2.1 Turbocharging in Diesel Engines......................... ^ 7.2.2 Identification of Charging Pressure Models ................ ^ 7.2.3 Performance Comparison to the GHSM .................. 7.2.4 Summary .......................................... jjg 7.3 Application to the Lateral Vehicle Motion Identification ........... j|g 7.3.1 Conventional System Modelling ........................
Contents Page VIÏ 7.3.2 Identification Based on the DMLP .........................120 7.3.3 Model Validation and Performance Comparison ...............121 7.3.4 Summary .............................................123 8. Model-Bank Oriented Monitoring......................................124 8.1 Residual Generation Based on Black-Box -Models .................124 8.2 Sensor Monitoring in a Thermal Plant.............................126 8.2.1 Model Bank Design.....................................126 8.2.2 Residual Pattern Generation ..............................129 8.2.3 Summary .............................................132 8.3 Monitoring of Lateral Vehicle Motion.............................133 8.3.1 Lock Up of Rear Axle ...................................133 8.3.2 Reduced Friction Coefficient..............................134 8.3.3 Summary .............................................136 9. Summary and Conclusions............................................137 Appendix ...............................................................139 Al. Generalized Delta-Rule, Error Backpropagation.....................139 A2. Interpolation with RBF Networks ................................142 A3. Generalized Hammerstein and Wiener Models......................144 A4. PETRA9....................................................147 Reference ..............................................................151
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| spelling | Ayoubi, Mihiar Verfasser aut Nonlinear system identification based on neural networks with locally distributed dynamics and application to technical processes Mihiar Ayoubi Als Ms. gedr. Düsseldorf VDI-Verl. 1996 X, 158 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Verein Deutscher Ingenieure: [Fortschritt-Berichte VDI / 8] 591 Zugl.: Darmstadt, Techn. Hochsch., Diss., 1996 Nichtlineares dynamisches System (DE-588)4126142-2 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Mehrschichten-Perzeptron (DE-588)4354626-2 gnd rswk-swf Systemidentifikation (DE-588)4121753-6 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Nichtlineares dynamisches System (DE-588)4126142-2 s Systemidentifikation (DE-588)4121753-6 s Neuronales Netz (DE-588)4226127-2 s DE-604 Mehrschichten-Perzeptron (DE-588)4354626-2 s 8] Verein Deutscher Ingenieure: [Fortschritt-Berichte VDI 591 (DE-604)BV000882482 591 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007397158&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Ayoubi, Mihiar Nonlinear system identification based on neural networks with locally distributed dynamics and application to technical processes Nichtlineares dynamisches System (DE-588)4126142-2 gnd Neuronales Netz (DE-588)4226127-2 gnd Mehrschichten-Perzeptron (DE-588)4354626-2 gnd Systemidentifikation (DE-588)4121753-6 gnd |
| subject_GND | (DE-588)4126142-2 (DE-588)4226127-2 (DE-588)4354626-2 (DE-588)4121753-6 (DE-588)4113937-9 |
| title | Nonlinear system identification based on neural networks with locally distributed dynamics and application to technical processes |
| title_auth | Nonlinear system identification based on neural networks with locally distributed dynamics and application to technical processes |
| title_exact_search | Nonlinear system identification based on neural networks with locally distributed dynamics and application to technical processes |
| title_full | Nonlinear system identification based on neural networks with locally distributed dynamics and application to technical processes Mihiar Ayoubi |
| title_fullStr | Nonlinear system identification based on neural networks with locally distributed dynamics and application to technical processes Mihiar Ayoubi |
| title_full_unstemmed | Nonlinear system identification based on neural networks with locally distributed dynamics and application to technical processes Mihiar Ayoubi |
| title_short | Nonlinear system identification based on neural networks with locally distributed dynamics and application to technical processes |
| title_sort | nonlinear system identification based on neural networks with locally distributed dynamics and application to technical processes |
| topic | Nichtlineares dynamisches System (DE-588)4126142-2 gnd Neuronales Netz (DE-588)4226127-2 gnd Mehrschichten-Perzeptron (DE-588)4354626-2 gnd Systemidentifikation (DE-588)4121753-6 gnd |
| topic_facet | Nichtlineares dynamisches System Neuronales Netz Mehrschichten-Perzeptron Systemidentifikation Hochschulschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007397158&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV000882482 |
| work_keys_str_mv | AT ayoubimihiar nonlinearsystemidentificationbasedonneuralnetworkswithlocallydistributeddynamicsandapplicationtotechnicalprocesses |