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.

中文翻译：

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基于Lyapunov稳定性-基于动态反向传播的不同类型功能链接神经网络的非线性系统比较研究

摘要

在本文中，已经对各种类型的功能链接神经网络（FLNN）的性能进行了比较，以进行非线性系统识别。在本研究中比较的FLNN是：三角FLNN，Legendre FLNN（LeFLNN），Chebyshev FLNN，幂级数FLNN（PSFLNN）和Hermite FLNN。递归权重调整方程是使用Lyapunov稳定性准则和动态反向传播算法的组合得出的。在仿真研究中，总共考虑了三个非线性系统（静态和动态系统）以测试和比较上述FLNN的逼近能力和计算复杂度。从仿真结果可以看出，与其余模型相比，LeFLNN具有更好的逼近精度，而PSFLNN提供的计算量最少。