Supervised learning with complex-valued neural networks:
<p>Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural ne...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2013
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| Schriftenreihe: | Studies in computational intelligence
421 |
| Schlagworte: | |
| Online-Zugang: | BTU01 FHA01 FHI01 FHN01 FHR01 FKE01 FWS01 UBY01 Volltext Inhaltsverzeichnis Abstract |
| Zusammenfassung: | <p>Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks. Furthermore, to efficiently preserve the physical characteristics of these complex-valued signals, it is important to develop complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The concepts of meta-cognition equipped with a self-regulated learning have been known to be the best human learning strategy. In this monograph, the principles of meta-cognition have been introduced for complex-valued neural networks in both the batch and sequential learning modes. For applications where the computation time of the training process is critical, a fast learning complex-valued neural network called as a fully complex-valued relaxation network along with its learning algorithm has been presented. The presence of orthogonal decision boundaries helps complex-valued neural networks to outperform real-valued networks in performing classification tasks. This aspect has been highlighted. The performances of various complex-valued neural networks are evaluated on a set of benchmark and real-world function approximation and real-valued classification problems.</p> |
| Beschreibung: | Introduction -- Fully Complex-valued Multi Layer Perceptron Networks -- Fully Complex-valued Radial Basis Function Networks -- Performance Study on Complex-valued Function Approximation Problems -- Circular Complex-valued Extreme Learning Machine Classifier -- Performance Study on Real-valued Classification Problems -- Complex-valued Self-regulatory Resource Allocation Network -- Conclusions and Scope for FutureWorks (CSRAN) |
| Beschreibung: | 1 Online-Ressource (XXII, 170 p. 37 illus) |
| ISBN: | 9783642294914 |
| DOI: | 10.1007/978-3-642-29491-4 |
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| adam_text | SUPERVISED LEARNING WITH COMPLEX-VALUED NEURAL NETWORKS
/ SURESH, SUNDARAM
: 2013
TABLE OF CONTENTS / INHALTSVERZEICHNIS
INTRODUCTION
FULLY COMPLEX-VALUED MULTI LAYER PERCEPTRON NETWORKS
FULLY COMPLEX-VALUED RADIAL BASIS FUNCTION NETWORKS
PERFORMANCE STUDY ON COMPLEX-VALUED FUNCTION APPROXIMATION PROBLEMS
CIRCULAR COMPLEX-VALUED EXTREME LEARNING MACHINE CLASSIFIER
PERFORMANCE STUDY ON REAL-VALUED CLASSIFICATION PROBLEMS
COMPLEX-VALUED SELF-REGULATORY RESOURCE ALLOCATION NETWORK
CONCLUSIONS AND SCOPE FOR FUTUREWORKS (CSRAN)
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
SUPERVISED LEARNING WITH COMPLEX-VALUED NEURAL NETWORKS
/ SURESH, SUNDARAM
: 2013
ABSTRACT / INHALTSTEXT
RECENT ADVANCEMENTS IN THE FIELD OF TELECOMMUNICATIONS, MEDICAL IMAGING
AND SIGNAL PROCESSING DEAL WITH SIGNALS THAT ARE INHERENTLY TIME
VARYING, NONLINEAR AND COMPLEX-VALUED. THE TIME VARYING, NONLINEAR
CHARACTERISTICS OF THESE SIGNALS CAN BE EFFECTIVELY ANALYZED USING
ARTIFICIAL NEURAL NETWORKS. FURTHERMORE, TO EFFICIENTLY PRESERVE THE
PHYSICAL CHARACTERISTICS OF THESE COMPLEX-VALUED SIGNALS, IT IS
IMPORTANT TO DEVELOP COMPLEX-VALUED NEURAL NETWORKS AND DERIVE THEIR
LEARNING ALGORITHMS TO REPRESENT THESE SIGNALS AT EVERY STEP OF THE
LEARNING PROCESS. THIS MONOGRAPH COMPRISES A COLLECTION OF NEW
SUPERVISED LEARNING ALGORITHMS ALONG WITH NOVEL ARCHITECTURES FOR
COMPLEX-VALUED NEURAL NETWORKS. THE CONCEPTS OF META-COGNITION EQUIPPED
WITH A SELF-REGULATED LEARNING HAVE BEEN KNOWN TO BE THE BEST HUMAN
LEARNING STRATEGY. IN THIS MONOGRAPH, THE PRINCIPLES OF META-COGNITION
HAVE BEEN INTRODUCED FOR COMPLEX-VALUED NEURAL NETWORKS IN BOTH THE
BATCH AND SEQUENTIAL LEARNING MODES. FOR APPLICATIONS WHERE THE
COMPUTATION TIME OF THE TRAINING PROCESS IS CRITICAL, A FAST LEARNING
COMPLEX-VALUED NEURAL NETWORK CALLED AS A FULLY COMPLEX-VALUED
RELAXATION NETWORK ALONG WITH ITS LEARNING ALGORITHM HAS BEEN PRESENTED.
THE PRESENCE OF ORTHOGONAL DECISION BOUNDARIES HELPS COMPLEX-VALUED
NEURAL NETWORKS TO OUTPERFORM REAL-VALUED NETWORKS IN PERFORMING
CLASSIFICATION TASKS. THIS ASPECT HAS BEEN HIGHLIGHTED. THE PERFORMANCES
OF VARIOUS COMPLEX-VALUED NEURAL NETWORKS ARE EVALUATED ON A SET OF
BENCHMARK AND REAL-WORLD FUNCTION APPROXIMATION AND REAL-VALUED
CLASSIFICATION PROBLEMS
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
|
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| discipline | Informatik |
| doi_str_mv | 10.1007/978-3-642-29491-4 |
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| indexdate | 2024-08-01T16:14:51Z |
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| language | English |
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| spellingShingle | Suresh, Sundaram Supervised learning with complex-valued neural networks Studies in computational intelligence Ingenieurwissenschaften Engineering Komplexe Zahl (DE-588)4128698-4 gnd Neuronales Netz (DE-588)4226127-2 gnd Überwachtes Lernen (DE-588)4580264-6 gnd |
| subject_GND | (DE-588)4128698-4 (DE-588)4226127-2 (DE-588)4580264-6 |
| title | Supervised learning with complex-valued neural networks |
| title_auth | Supervised learning with complex-valued neural networks |
| title_exact_search | Supervised learning with complex-valued neural networks |
| title_full | Supervised learning with complex-valued neural networks Sundaram Suresh ; Narasimhan Sundararajan ; Ramasamy Savitha |
| title_fullStr | Supervised learning with complex-valued neural networks Sundaram Suresh ; Narasimhan Sundararajan ; Ramasamy Savitha |
| title_full_unstemmed | Supervised learning with complex-valued neural networks Sundaram Suresh ; Narasimhan Sundararajan ; Ramasamy Savitha |
| title_short | Supervised learning with complex-valued neural networks |
| title_sort | supervised learning with complex valued neural networks |
| topic | Ingenieurwissenschaften Engineering Komplexe Zahl (DE-588)4128698-4 gnd Neuronales Netz (DE-588)4226127-2 gnd Überwachtes Lernen (DE-588)4580264-6 gnd |
| topic_facet | Ingenieurwissenschaften Engineering Komplexe Zahl Neuronales Netz Überwachtes Lernen |
| url | https://doi.org/10.1007/978-3-642-29491-4 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025781650&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=025781650&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV020822171 |
| work_keys_str_mv | AT sureshsundaram supervisedlearningwithcomplexvaluedneuralnetworks AT sundararajannarasimhan supervisedlearningwithcomplexvaluedneuralnetworks AT savitharamasamy supervisedlearningwithcomplexvaluedneuralnetworks |