Hierarchical modular granular neural networks with fuzzy aggregation:
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
[Cham]
Springer International Publishing
[2016]
|
| Schriftenreihe: | SpringerBriefs in applied sciences and technology
|
| Schlagworte: | |
| Online-Zugang: | BTU01 FAB01 FAW01 FHA01 FHI01 FHN01 FHR01 FKE01 FRO01 FWS01 FWS02 UBY01 Volltext Abstract Inhaltsverzeichnis |
| Beschreibung: | 1 Online Ressource (VIII, 101 Seiten) |
| ISBN: | 9783319288628 |
| DOI: | 10.1007/978-3-319-28862-8 |
Internformat
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Datensatz im Suchindex
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| adam_text | HIERARCHICAL MODULAR GRANULAR NEURAL NETWORKS WITH FUZZY AGGREGATION
/ SANCHEZ, DANIELA
: 2016
ABSTRACT / INHALTSTEXT
IN THIS BOOK, A NEW METHOD FOR HYBRID INTELLIGENT SYSTEMS IS PROPOSED.
THE PROPOSED METHOD IS BASED ON A GRANULAR COMPUTING APPROACH APPLIED IN
TWO LEVELS. THE TECHNIQUES USED AND COMBINED IN THE PROPOSED METHOD ARE
MODULAR NEURAL NETWORKS (MNNS) WITH A GRANULAR COMPUTING (GRC) APPROACH,
THUS RESULTING IN A NEW CONCEPT OF MNNS; MODULAR GRANULAR NEURAL
NETWORKS (MGNNS). IN ADDITION FUZZY LOGIC (FL) AND HIERARCHICAL GENETIC
ALGORITHMS (HGAS) ARE TECHNIQUES USED IN THIS RESEARCH WORK TO IMPROVE
RESULTS. THESE TECHNIQUES ARE CHOSEN BECAUSE IN OTHER WORKS HAVE
DEMONSTRATED TO BE A GOOD OPTION, AND IN THE CASE OF MNNS AND HGAS,
THESE TECHNIQUES ALLOW TO IMPROVE THE RESULTS OBTAINED THAN WITH THEIR
CONVENTIONAL VERSIONS; RESPECTIVELY ARTIFICIAL NEURAL NETWORKS AND
GENETIC ALGORITHMS
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
HIERARCHICAL MODULAR GRANULAR NEURAL NETWORKS WITH FUZZY AGGREGATION
/ SANCHEZ, DANIELA
: 2016
TABLE OF CONTENTS / INHALTSVERZEICHNIS
INTRODUCTION
BACKGROUND AND THEORY
PROPOSED METHOD
APPLICATION TO HUMAN RECOGNITION
EXPERIMENTAL RESULTS
CONCLUSIONS
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
|
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| dewey-full | 006.3 |
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| dewey-ones | 006 - Special computer methods |
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| illustrated | Not Illustrated |
| indexdate | 2024-08-01T12:13:02Z |
| institution | BVB |
| isbn | 9783319288628 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028840663 |
| oclc_num | 943819585 |
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| psigel | ZDB-2-ENG ZDB-2-ENG_2016 |
| publishDate | 2016 |
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| publisher | Springer International Publishing |
| record_format | marc |
| series2 | SpringerBriefs in applied sciences and technology |
| spellingShingle | Sánchez, Daniela Hierarchical modular granular neural networks with fuzzy aggregation Engineering Artificial intelligence Neural networks (Computer science) Computational intelligence Computational Intelligence Artificial Intelligence (incl. Robotics) Mathematical Models of Cognitive Processes and Neural Networks Ingenieurwissenschaften Künstliche Intelligenz |
| title | Hierarchical modular granular neural networks with fuzzy aggregation |
| title_auth | Hierarchical modular granular neural networks with fuzzy aggregation |
| title_exact_search | Hierarchical modular granular neural networks with fuzzy aggregation |
| title_full | Hierarchical modular granular neural networks with fuzzy aggregation Daniela Sanchez, Patricia Melin |
| title_fullStr | Hierarchical modular granular neural networks with fuzzy aggregation Daniela Sanchez, Patricia Melin |
| title_full_unstemmed | Hierarchical modular granular neural networks with fuzzy aggregation Daniela Sanchez, Patricia Melin |
| title_short | Hierarchical modular granular neural networks with fuzzy aggregation |
| title_sort | hierarchical modular granular neural networks with fuzzy aggregation |
| topic | Engineering Artificial intelligence Neural networks (Computer science) Computational intelligence Computational Intelligence Artificial Intelligence (incl. Robotics) Mathematical Models of Cognitive Processes and Neural Networks Ingenieurwissenschaften Künstliche Intelligenz |
| topic_facet | Engineering Artificial intelligence Neural networks (Computer science) Computational intelligence Computational Intelligence Artificial Intelligence (incl. Robotics) Mathematical Models of Cognitive Processes and Neural Networks Ingenieurwissenschaften Künstliche Intelligenz |
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