Paper Info
Title
Lyapunov stability-Dynamic Back Propagation-based comparative study of different types of functional link neural networks for the identification of nonlinear systems
Abstract
In this paper, the performance comparison of various types of functional link neural networks (FLNNs) has been done for the nonlinear system identification. The FLNNs being compared in the present study are: trigonometry FLNN, Legendre FLNN (LeFLNN), Chebyshev FLNN, power series FLNN (PSFLNN) and Hermite FLNN. The recursive weights adjustment equations are derived using the combination of Lyapunov stability criterion and dynamic back propagation algorithm. In the simulation study, a total of three nonlinear systems (both static and dynamic systems) are considered for testing and comparing the approximation ability and computational complexity of the above-mentioned FLNNs. From the simulation results, it is observed that the LeFLNN has given better approximation accuracy and PSFLNN offered least computational load as compared to the rest models.
Year
DOI
Venue
2020
10.1007/s00500-019-04496-0
Soft Computing
Keywords
Field
DocType
Functional link neural network, Nonlinear systems, Dynamic back propagation algorithm, Identification, Lyapunov stability analysis, Adaptive learning rate
Mathematical optimization,Nonlinear system,Control theory,Computer science,Lyapunov stability,Nonlinear system identification,Chebyshev filter,Backpropagation,Artificial neural network,Dynamical system,Computational complexity theory
Journal
Volume
Issue
ISSN
24
7
1432-7643
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Rajesh Kumar1129.32
Smriti Srivastava213719.60
Amit Mohindru351.06