Predictive Modular Neural Networks: Applications to Time Series
The subject of this book is predictive modular neural networks and their ap plication to time series problems: classification, prediction and identification. The intended audience is researchers and graduate students in the fields of neural networks, computer science, statistical pattern recognitio...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
1998
|
| Schriftenreihe: | The Springer International Series in Engineering and Computer Science
466 |
| Schlagworte: | |
| Online-Zugang: | BTU01 Volltext |
| Zusammenfassung: | The subject of this book is predictive modular neural networks and their ap plication to time series problems: classification, prediction and identification. The intended audience is researchers and graduate students in the fields of neural networks, computer science, statistical pattern recognition, statistics, control theory and econometrics. Biologists, neurophysiologists and medical engineers may also find this book interesting. In the last decade the neural networks community has shown intense interest in both modular methods and time series problems. Similar interest has been expressed for many years in other fields as well, most notably in statistics, control theory, econometrics etc. There is a considerable overlap (not always recognized) of ideas and methods between these fields. Modular neural networks come by many other names, for instance multiple models, local models and mixtures of experts. The basic idea is to independently develop several "subnetworks" (modules), which may perform the same or re lated tasks, and then use an "appropriate" method for combining the outputs of the subnetworks. Some of the expected advantages of this approach (when compared with the use of "lumped" or "monolithic" networks) are: superior performance, reduced development time and greater flexibility. For instance, if a module is removed from the network and replaced by a new module (which may perform the same task more efficiently), it should not be necessary to retrain the aggregate network |
| Beschreibung: | 1 Online-Ressource (XI, 314 p) |
| ISBN: | 9781461555551 |
| DOI: | 10.1007/978-1-4615-5555-1 |
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| spelling | Petridis, Vassilios Verfasser aut Predictive Modular Neural Networks Applications to Time Series by Vassilios Petridis, Athanasios Kehagias Boston, MA Springer US 1998 1 Online-Ressource (XI, 314 p) txt rdacontent c rdamedia cr rdacarrier The Springer International Series in Engineering and Computer Science 466 The subject of this book is predictive modular neural networks and their ap plication to time series problems: classification, prediction and identification. The intended audience is researchers and graduate students in the fields of neural networks, computer science, statistical pattern recognition, statistics, control theory and econometrics. Biologists, neurophysiologists and medical engineers may also find this book interesting. In the last decade the neural networks community has shown intense interest in both modular methods and time series problems. Similar interest has been expressed for many years in other fields as well, most notably in statistics, control theory, econometrics etc. There is a considerable overlap (not always recognized) of ideas and methods between these fields. Modular neural networks come by many other names, for instance multiple models, local models and mixtures of experts. The basic idea is to independently develop several "subnetworks" (modules), which may perform the same or re lated tasks, and then use an "appropriate" method for combining the outputs of the subnetworks. Some of the expected advantages of this approach (when compared with the use of "lumped" or "monolithic" networks) are: superior performance, reduced development time and greater flexibility. For instance, if a module is removed from the network and replaced by a new module (which may perform the same task more efficiently), it should not be necessary to retrain the aggregate network Physics Statistical Physics, Dynamical Systems and Complexity Electrical Engineering Data Structures, Cryptology and Information Theory Mechanical Engineering Data structures (Computer science) Statistical physics Dynamical systems Mechanical engineering Electrical engineering Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 s Neuronales Netz (DE-588)4226127-2 s 1\p DE-604 Kehagias, Athanasios aut Erscheint auch als Druck-Ausgabe 9781461375401 https://doi.org/10.1007/978-1-4615-5555-1 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Petridis, Vassilios Kehagias, Athanasios Predictive Modular Neural Networks Applications to Time Series Physics Statistical Physics, Dynamical Systems and Complexity Electrical Engineering Data Structures, Cryptology and Information Theory Mechanical Engineering Data structures (Computer science) Statistical physics Dynamical systems Mechanical engineering Electrical engineering Zeitreihenanalyse (DE-588)4067486-1 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| subject_GND | (DE-588)4067486-1 (DE-588)4226127-2 |
| title | Predictive Modular Neural Networks Applications to Time Series |
| title_auth | Predictive Modular Neural Networks Applications to Time Series |
| title_exact_search | Predictive Modular Neural Networks Applications to Time Series |
| title_full | Predictive Modular Neural Networks Applications to Time Series by Vassilios Petridis, Athanasios Kehagias |
| title_fullStr | Predictive Modular Neural Networks Applications to Time Series by Vassilios Petridis, Athanasios Kehagias |
| title_full_unstemmed | Predictive Modular Neural Networks Applications to Time Series by Vassilios Petridis, Athanasios Kehagias |
| title_short | Predictive Modular Neural Networks |
| title_sort | predictive modular neural networks applications to time series |
| title_sub | Applications to Time Series |
| topic | Physics Statistical Physics, Dynamical Systems and Complexity Electrical Engineering Data Structures, Cryptology and Information Theory Mechanical Engineering Data structures (Computer science) Statistical physics Dynamical systems Mechanical engineering Electrical engineering Zeitreihenanalyse (DE-588)4067486-1 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| topic_facet | Physics Statistical Physics, Dynamical Systems and Complexity Electrical Engineering Data Structures, Cryptology and Information Theory Mechanical Engineering Data structures (Computer science) Statistical physics Dynamical systems Mechanical engineering Electrical engineering Zeitreihenanalyse Neuronales Netz |
| url | https://doi.org/10.1007/978-1-4615-5555-1 |
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