Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks

By Jagdish Chandra Patra, Alex ChiChung Kot. IEEE Trans. Syst. Man Cybern. Part B, 2002

342 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/cb08f48fcc150ce5d498e1bb39677cedaed5eefd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Zeng-Guang Hou
2. Xi Liu
3. Krishna Dev Kumar
4. Anmin Zou
5. Jagdish Chandra Patra
6. Ranjeeta Bisoi
7. Pradipta Kishore Dash
8. Ajit Kumar Rout

In Control and Systems Engineering

1. Zeng-Guang Hou
2. Xi Liu
3. Krishna Dev Kumar
4. Anmin Zou
5. Yuanqing Xia
6. Kunfeng Lu
7. Frank L. Lewis
8. Shaocheng Tong

In Signal Processing

1. Zeng-Guang Hou
2. Xi Liu
3. Krishna Dev Kumar
4. Anmin Zou

Citations in the corpus

• Nonlinear dynamic system identification using Legendre neural network (2010, background): “The ChNN has been extensively applied for nonlinear dynamic system identification [13], [14], [15], [16], digital communication channel equalization [17], [18], [19] and intelligent sensors [20], with quite satisfactory results.”
• FAT-based robust adaptive control of electrically driven robots without velocity measurements (2017, neutral): “The detailed description of CNN and its approximation properties can be found in [34].”
• Finite-Time Attitude Tracking Control for Spacecraft Using Terminal Sliding Mode and Chebyshev Neural Network (2011, positive): “In particular, Chebyshev NN (CNN) is a functional link network (FLN) whose input is generated by using a subset of Chebyshev polynomials, and it has been shown that CNN has powerful representation capabilities [14].”