Advanced methods for fusion of fuzzy systems and neural networks in intelligent data processing:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
Düsseldorf
VDI-Verl.
1996
|
| Ausgabe: | Als Ms. gedr. |
| Schriftenreihe: | Verein Deutscher Ingenieure: [Fortschrittberichte VDI / 10]
401 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | VIII, 175 S. Ill., graph. Darst. |
| ISBN: | 318340110X |
Internformat
MARC
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Datensatz im Suchindex
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| adam_text | Titel: Advanced methods for fusion of fuzzy systems and neural networks in intelligent data processing
Autor: Halgamuge, Saman
Jahr: 1996
Contents 1 Introduction 1 1.1 Background and Motivation............................. 1 1.2 Methods of Computational Intelligence....................... 2 1.2.1 Fuzzy Systems ................................ 2 1.2.2 Neural Networks............................... 4 1.2.3 Evolutionary Algorithms .......................... 5 1.3 Thesis Objective ................................... 7 1.4 Thesis Organization.................................. 8 2 Advanced Methods of Fusion 10 2.1 Methods of Fusion .................................. 10 2.2 Fuzzy Neural Networks - An Overview....................... 13 2.2.1 Fuzzy Systems as Feed Forward Neural Networks............ . 14 2.2.2 Input Space Partition in Generating Fuzzy Systems............ 19 2.2.3 Neural Networks Equivalent to TSK Fuzzy Systems ........... 21 2.2.4 Neural Networks Equivalent to FuNe I Fuzzy Systems .......... 23 2.2.5 Neural Networks Equivalent to Mamdani Type Fuzzy Controllers ..... 24 2.2.6 Neural Networks Equivalent to Classifier Type Fuzzy Systems...... 25 2.2.7 Adaptive Fuzzy Systems Based on Reinforcement Learning ....... 27 2.3 Fusion Techniques Based on Evolutionary Algorithms .............. 28 2.3.1 Construction of Optimal Neural Network Architectures.......... 29 2.3.2 Genetic Design of a Fuzzy Controller.................... 29 3 Application and Test with Combined Systems 31 3.1 Benchmarks...................................... 31 3.1.1 Artificially Generated Data ......................... 31 3.1.2 Iris Species .................................. 32 3.2 Industrial Quality Control.............................. 34 3.2.1 Solder Joint Recognition........................... 34 3.2.2 Sonar Data Identification .......................... 35 3.3 The Mechatronic Systems.............................. 36 3.3.1 Intelligent Tyres ............................... 37 V
3.3.2 Reverse Driving of a Truck with a Long Trailer.............. 37 3.3.3 Autonomous Driving of Mobile Robot with Collision Avoidance..... 41 3.4 Handwritten Numerical Character Recognition .................. 45 4 Fuzzy Systems with Gradient Descent Learning 47 4.1 Methods of Fuzzy Rule extraction ......................... 47 4.1.1 FuNe I Fuzzy Model............................. 47 4.1.2 Method Proposed for Complex Applications................ 48 4.1.3 Identification of Rule Relevant Nodes.................... 49 4.2 Towards an Optimized Solution............................ 51 4.2.1 Parameter Tuning............................... 51 4.2.2 Rule Optimization.............................. 52 4.2.3 Posterior Input Feature Reduction ..................... 52 4.2.4 Knowledge Integration............................ 52 4.3 Summary of Applications of FuNe I ........................ 52 4.3.1 Iris Species Classifier ............................ 53 4.3.2 State Identification.............................. 54 4.3.3 Image Classification.............................. 55 4.4 Real Time Solutions for FuNe I system....................... 58 4.5 Conclusion ...................................... 58 5 Radial Basis Function Network-Fuzzy Systems 59 5.1 RBFN with RCE Learning ............................. 59 5.2 CBFN with MRCE Learning ............................ 62 5.3 Training RBF and CBF Networks ......................... 64 5.4 Fuzzy Rule Based Classification.........................? . . 65 5.4.1 Removal of Superfluous Inputs ....................... 66 5.4.2 Methods for the Segmentation of Input Space............... 67 5.4.3 Antecedent Membership Functions..................... 68 5.4.4 Fuzzy Rule Generation ........................... 70 5.5 Simulation Results.................................. 71 5.6 Conclusion ...................................... 74 6 Fuzzy interpretable Neural Clustering 75 6.1 Nearest Prototype
Clustering............................ 75 6.2 Fuzzy Interpretation of nearest prototype clustering................ 76 6.3 Dynamically Growing Nearest Prototypes ..................... 78 6.3.1 The Geometrical Method - DVQ2...................... 79 6.3.2 Sub-Bayesian Approximation - DVQ3 ................... 79 6.4 Applicability of the Method............................. 81 6.4.1 Simulation results .............................. 81 6.4.2 Possible Improvements............................ 83 VI
7 Rule Based Classifiers with Genetic Algorithms 84 7.1 Rule Based Classification............................... 84 7.2 A Heuristic Method.................................. 84 7.3 Genetic Algorithm Based Method.......................... 87 7.3.1 Encoding ................................... 87 7.3.2 Genetic Operators .............................. 87 7.3.3 Fitness Evaluation.............................. 88 7.4 Results......................................... 89 7.4.1 Heuristic Method............................... 89 7.4.2 Genetic Algorithm Based Solutions..................... 90 7.5 Further Improvements................................ 95 7.5.1 Improving the Heuristic Method....................... 95 8 CBADD-Uni versai Defuzzification Approximator 100 8.1 Conventional Methods............... 100 8.2 Problems Incurred with Existing Methods.....................101 8.3 A Customizable Universal Defuzzification Approximator .............104 8.3.1 Gradient Descent Learning with Alternative Neuron Models.......105 8.3.2 Implementation as a Transparent Neural Network.............108 8.4 Validation.......................................110 8.4.1 Validation with COG ............................110 8.4.2 Validation with MOM ............................112 8.5 Application......................................113 8.5.1 Approximation of MOA ...........................113 8.5.2 Approximation of COM ...........................113 8.5.3 Application in Truck and Trailer ......................113 8.6 Integration in a Fuzzy Neural Controller......................114 8.7 Conclusion ......................................116 9 Fuzzy Controlled Neural Networks 119 9.1 Speed Influencing Parameters in Backpropagation.................119 9.2 Parameter Adaptation in Standard Backpropagation ...............120 9.3 Modified Gradient Descent Algorithms.......................123 9.3.1 Fuzzy Controlled Quickpropagation ....................123 9.3.2
Algorithm of Individual Learning Rates ..................124 9.3.3 Quickpropagation with Individual Learning Rates.............125 9.4 Simulation Results..................................125 9.4.1 Fuzzy Controlled Backpropagation Methods................126 9.4.2 Modified Gradient Descent Algorithms...................128 9.5 Conclusion ......................................131 10 Hardware Implementations 132 VII
10.1 Rapid Prototyping of Fuzzy Systems........................132 10.1.1 Fuzzification Unit.............................. . 134 10.1.2 Rule Inference.................................135 10.1.3 Defuzzification ................................135 10.1.4 Application Example.............................137 10.1.5 Implementation of Automatically Generated Fuzzy Systems .......137 10.2 Realtime Fuzzy Interpretable Classifiers ......................138 10.2.1 Computationally Feasible Distance Measures................139 10.2.2 Fixed Point Calculation...........................140 10.2.3 FPGA Architecture for Parallel Processing.................140 10.2.4 SIMD Array with FPGAs..........................144 10.3 Conclusion.......................................147 11 Conclusion 148 11.1 Different methods for different problems......................149 11.2 Methods of Hardware Implementation .......................150 A FuNeGen Software 152 A.l Introduction......................................152 A.2 Software Tool FuNeGen 1.0 .............................152 A.3 Extended Version FuNe Deluxe...........................153 References 157 VIII
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| indexdate | 2024-07-09T17:56:35Z |
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| series2 | Verein Deutscher Ingenieure: [Fortschrittberichte VDI / 10] |
| spelling | Halgamuge, Saman Verfasser aut Advanced methods for fusion of fuzzy systems and neural networks in intelligent data processing Saman Halgamuge Als Ms. gedr. Düsseldorf VDI-Verl. 1996 VIII, 175 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Verein Deutscher Ingenieure: [Fortschrittberichte VDI / 10] 401 Zugl.: Darmstadt, Techn. Hochsch., Diss., 1994 Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Fuzzy-Logik (DE-588)4341284-1 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Neuronales Netz (DE-588)4226127-2 s Fuzzy-Logik (DE-588)4341284-1 s DE-604 10] Verein Deutscher Ingenieure: [Fortschrittberichte VDI 401 (DE-604)BV000897204 401 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007105794&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Halgamuge, Saman Advanced methods for fusion of fuzzy systems and neural networks in intelligent data processing Neuronales Netz (DE-588)4226127-2 gnd Fuzzy-Logik (DE-588)4341284-1 gnd |
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| title | Advanced methods for fusion of fuzzy systems and neural networks in intelligent data processing |
| title_auth | Advanced methods for fusion of fuzzy systems and neural networks in intelligent data processing |
| title_exact_search | Advanced methods for fusion of fuzzy systems and neural networks in intelligent data processing |
| title_full | Advanced methods for fusion of fuzzy systems and neural networks in intelligent data processing Saman Halgamuge |
| title_fullStr | Advanced methods for fusion of fuzzy systems and neural networks in intelligent data processing Saman Halgamuge |
| title_full_unstemmed | Advanced methods for fusion of fuzzy systems and neural networks in intelligent data processing Saman Halgamuge |
| title_short | Advanced methods for fusion of fuzzy systems and neural networks in intelligent data processing |
| title_sort | advanced methods for fusion of fuzzy systems and neural networks in intelligent data processing |
| topic | Neuronales Netz (DE-588)4226127-2 gnd Fuzzy-Logik (DE-588)4341284-1 gnd |
| topic_facet | Neuronales Netz Fuzzy-Logik Hochschulschrift |
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