Lyapunov stability-Dynamic Back Propagation-based comparative study of different types of functional link neural networks for the identification of nonlinear systems

被引：0

作者：

Rajesh Kumar

Smriti Srivastava

Amit Mohindru

机构：

[1] Thapar Institute of Engineering and Technology (Deemed to be University),Department of Electrical and Instrumentation Engineering

[2] Netaji Subhas University of Technology (formerly Netaji Subhas Institute of Technology),Division of Instrumentation and Control Engineering

[3] Indraprastha Institute of Information Technology,Department of Electronics and Communication Engineering

来源：

Soft Computing | 2020年 / 24卷

关键词：

Functional link neural network; Nonlinear systems; Dynamic back propagation algorithm; Identification; Lyapunov stability analysis; Adaptive learning rate;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

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.

引用

页码：5463 / 5482

页数：19

共 30 条

[1] Lyapunov stability-Dynamic Back Propagation-based comparative study of different types of functional link neural networks for the identification of nonlinear systems
Kumar, Rajesh
Srivastava, Smriti
Mohindru, Amit
SOFT COMPUTING, 2020, 24 (07) : 5463 - 5482
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[3] A novel dynamic recurrent functional link neural network-based identification of nonlinear systems using Lyapunov stability analysis
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[5] Comparative study of neural networks for dynamic nonlinear systems identification
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