Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
2014
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| Schriftenreihe: | Studies in Computational Intelligence
510 |
| Schlagworte: | |
| Online-Zugang: | BTU01 FHA01 FHI01 FHN01 FHR01 FKE01 FRO01 FWS01 FWS02 UBY01 Volltext Inhaltsverzeichnis Abstract |
| Beschreibung: | The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practical applications. |
| Beschreibung: | 1 Online-Ressource (XXI, 182 p.) 125 illus |
| ISBN: | 9783319015477 |
| DOI: | 10.1007/978-3-319-01547-7 |
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| 500 | |a The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practical applications. | ||
| 505 | 0 | |a Introduction -- Designing of dynamic neural networks -- Estimation methods in training of ANNs for robust fault diagnosis -- MLP in robust fault detection of static non-linear systems -- GMDH networks in robust fault detection of dynamic non-linear systems -- State-space GMDH networks for actuator robust FDI. | |
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Datensatz im Suchindex
| DE-BY-FWS_katkey | 1015287 |
|---|---|
| _version_ | 1824553635921526785 |
| adam_text | ADVANCED NEURAL NETWORK-BASED COMPUTATIONAL SCHEMES FOR ROBUST FAULT
DIAGNOSIS
/ MRUGALSKI, MARCIN
: 2014
TABLE OF CONTENTS / INHALTSVERZEICHNIS
INTRODUCTION
DESIGNING OF DYNAMIC NEURAL NETWORKS
ESTIMATION METHODS IN TRAINING OF ANNS FOR ROBUST FAULT DIAGNOSIS
MLP IN ROBUST FAULT DETECTION OF STATIC NON-LINEAR SYSTEMS
GMDH NETWORKS IN ROBUST FAULT DETECTION OF DYNAMIC NON-LINEAR SYSTEMS
STATE-SPACE GMDH NETWORKS FOR ACTUATOR ROBUST FDI.
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
ADVANCED NEURAL NETWORK-BASED COMPUTATIONAL SCHEMES FOR ROBUST FAULT
DIAGNOSIS
/ MRUGALSKI, MARCIN
: 2014
ABSTRACT / INHALTSTEXT
THE PRESENT BOOK IS DEVOTED TO PROBLEMS OF ADAPTATION OF ARTIFICIAL
NEURAL NETWORKS TO ROBUST FAULT DIAGNOSIS SCHEMES. IT PRESENTS NEURAL
NETWORKS-BASED MODELLING AND ESTIMATION TECHNIQUES USED FOR DESIGNING
ROBUST FAULT DIAGNOSIS SCHEMES FOR NON-LINEAR DYNAMIC SYSTEMS. A PART OF
THE BOOK FOCUSES ON FUNDAMENTAL ISSUES SUCH AS ARCHITECTURES OF DYNAMIC
NEURAL NETWORKS, METHODS FOR DESIGNING OF NEURAL NETWORKS AND FAULT
DIAGNOSIS SCHEMES AS WELL AS THE IMPORTANCE OF ROBUSTNESS. THE BOOK IS
OF A TUTORIAL VALUE AND CAN BE PERCEIVED AS A GOOD STARTING POINT FOR
THE NEW-COMERS TO THIS FIELD. THE BOOK IS ALSO DEVOTED TO ADVANCED
SCHEMES OF DESCRIPTION OF NEURAL MODEL UNCERTAINTY. IN PARTICULAR, THE
METHODS OF COMPUTATION OF NEURAL NETWORKS UNCERTAINTY WITH ROBUST
PARAMETER ESTIMATION ARE PRESENTED. MOREOVER, A NOVEL APPROACH FOR
SYSTEM IDENTIFICATION WITH THE STATE-SPACE GMDH NEURAL NETWORK IS
DELIVERED. ALL THE CONCEPTS DESCRIBED IN THIS BOOK ARE ILLUSTRATED BY
BOTH SIMPLE ACADEMIC ILLUSTRATIVE EXAMPLES AND PRACTICAL APPLICATIONS.
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
|
| any_adam_object | 1 |
| author | Mrugalski, Marcin |
| author_facet | Mrugalski, Marcin |
| author_role | aut |
| author_sort | Mrugalski, Marcin |
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| bvnumber | BV041470953 |
| collection | ZDB-2-ENG |
| contents | Introduction -- Designing of dynamic neural networks -- Estimation methods in training of ANNs for robust fault diagnosis -- MLP in robust fault detection of static non-linear systems -- GMDH networks in robust fault detection of dynamic non-linear systems -- State-space GMDH networks for actuator robust FDI. |
| ctrlnum | (OCoLC)891516789 (DE-599)BVBBV041470953 |
| dewey-full | 006.3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3 |
| dewey-search | 006.3 |
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| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| doi_str_mv | 10.1007/978-3-319-01547-7 |
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| id | DE-604.BV041470953 |
| illustrated | Not Illustrated |
| indexdate | 2025-02-20T06:39:01Z |
| institution | BVB |
| isbn | 9783319015477 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-026917096 |
| oclc_num | 891516789 |
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| owner_facet | DE-Aug4 DE-92 DE-634 DE-859 DE-898 DE-BY-UBR DE-573 DE-861 DE-706 DE-863 DE-BY-FWS DE-862 DE-BY-FWS |
| physical | 1 Online-Ressource (XXI, 182 p.) 125 illus |
| psigel | ZDB-2-ENG |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| record_format | marc |
| series | Studies in Computational Intelligence |
| series2 | Studies in Computational Intelligence |
| spellingShingle | Mrugalski, Marcin Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis Studies in Computational Intelligence Introduction -- Designing of dynamic neural networks -- Estimation methods in training of ANNs for robust fault diagnosis -- MLP in robust fault detection of static non-linear systems -- GMDH networks in robust fault detection of dynamic non-linear systems -- State-space GMDH networks for actuator robust FDI. Engineering Artificial intelligence Physics Computational Intelligence Artificial Intelligence (incl. Robotics) Complexity Control Ingenieurwissenschaften Künstliche Intelligenz |
| title | Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis |
| title_auth | Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis |
| title_exact_search | Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis |
| title_full | Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis by Marcin Mrugalski |
| title_fullStr | Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis by Marcin Mrugalski |
| title_full_unstemmed | Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis by Marcin Mrugalski |
| title_short | Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis |
| title_sort | advanced neural network based computational schemes for robust fault diagnosis |
| topic | Engineering Artificial intelligence Physics Computational Intelligence Artificial Intelligence (incl. Robotics) Complexity Control Ingenieurwissenschaften Künstliche Intelligenz |
| topic_facet | Engineering Artificial intelligence Physics Computational Intelligence Artificial Intelligence (incl. Robotics) Complexity Control Ingenieurwissenschaften Künstliche Intelligenz |
| url | https://doi.org/10.1007/978-3-319-01547-7 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026917096&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026917096&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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