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System Identification Of Nonlinear Chaotic Signals
Using Neural Network Approach
Sagar Dhoble
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- 172 pages
- English
- PDF
- Available on iOS & Android
eBook - PDF
No longer available |Learn more
System Identification Of Nonlinear Chaotic Signals
Using Neural Network Approach
Sagar Dhoble
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About This Book
Selection of the topology of a neural network and correct parameters for the learning algorithm is a tedious task for designing an optimal artificial neural network, which is smaller, faster and with a better generalization performance. This book is my effort to mainly showcase the capability of time series prediction using neural network approach to predict complex Nonlinear datasets for forecasting. Experiments were carried out on Netflow series. It is observed that these problems exhibit a rich chaotic behavior and also leads to strange attractor. Multi-step ahead predictions have been carried out and the proposed neural network models have been optimized.
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Table of contents
Citation styles for System Identification Of Nonlinear Chaotic Signals
APA 6 Citation
Dhoble, S. (2013). System Identification Of Nonlinear Chaotic Signals (1st ed.). LAP LAMBERT Academic Publishing. Retrieved from https://www.perlego.com/book/3308556/system-identification-of-nonlinear-chaotic-signals-using-neural-network-approach-pdf (Original work published 2013)
Chicago Citation
Dhoble, Sagar. (2013) 2013. System Identification Of Nonlinear Chaotic Signals. 1st ed. LAP LAMBERT Academic Publishing. https://www.perlego.com/book/3308556/system-identification-of-nonlinear-chaotic-signals-using-neural-network-approach-pdf.
Harvard Citation
Dhoble, S. (2013) System Identification Of Nonlinear Chaotic Signals. 1st edn. LAP LAMBERT Academic Publishing. Available at: https://www.perlego.com/book/3308556/system-identification-of-nonlinear-chaotic-signals-using-neural-network-approach-pdf (Accessed: 15 October 2022).
MLA 7 Citation
Dhoble, Sagar. System Identification Of Nonlinear Chaotic Signals. 1st ed. LAP LAMBERT Academic Publishing, 2013. Web. 15 Oct. 2022.