Fundamentals of Higher Order Neural Networks for Modeling and Simulation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F12%3A00196111" target="_blank" >RIV/68407700:21220/12:00196111 - isvavai.cz</a>
Result on the web
<a href="http://www.igi-global.com/chapter/fundamentals-higher-order-neural-networks/71797" target="_blank" >http://www.igi-global.com/chapter/fundamentals-higher-order-neural-networks/71797</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.4018/978-1-4666-2175-6.ch006" target="_blank" >10.4018/978-1-4666-2175-6.ch006</a>
Alternative languages
Result language
angličtina
Original language name
Fundamentals of Higher Order Neural Networks for Modeling and Simulation
Original language description
In this chapter, we provide fundamental principles of higher order neural units (HONUs) and higher order neural networks (HONNs) for modeling and simulation. An essential core of HONNs can be found in higher order weighted combinations or correlations between the input variables and HONU. Except the high quality of nonlinear approximation of static HONUs, the capability of dynamic HONUs for modeling of dynamic systems is shown and compared to conventional recurrent neural networks when a practical learning algorithm is used. Also, the potential of continuous dynamic HONUs to approximate high dynamic-order systems is discussed as adaptable time delays can be implemented. By using some typical examples, this chapter describes how and why higher order combinations or correlations can be effective for modeling of systems.
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
CEP classification
BC - Theory and management systems
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
Artificial Higher Order Neural Networks for Modeling and Simulation
ISBN
978-1-4666-2175-6
Number of pages of the result
31
Pages from-to
103-133
Number of pages of the book
454
Publisher name
IGI Global
Place of publication
Hershey, Pennsylvania
UT code for WoS chapter
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