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Linear Approximation in Frequency Domain

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Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks.

J C Patra¹, A C Kot

¹Nanyang Technol. Univ.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 2, 2008

Summary

This study introduces a functional-link artificial neural network (FLANN) for dynamic nonlinear system identification, offering a computationally efficient alternative to traditional multilayer perceptrons (MLPs). The FLANN demonstrates comparable or superior performance with significantly reduced computational demands.

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Area of Science:

Artificial Intelligence
Computational Neuroscience
System Identification

Background:

Multilayer perceptrons (MLPs) trained with backpropagation (BP) are computationally intensive for learning.
Dynamic nonlinear system identification requires efficient computational models.
Feedforward neural networks often necessitate substantial computational resources.

Purpose of the Study:

To propose a computationally efficient artificial neural network (ANN) for dynamic nonlinear system identification.
To overcome the computational drawbacks of traditional MLPs.
To introduce a novel single-layer functional-link ANN (FLANN).

Main Methods:

Developed a single-layer functional-link artificial neural network (FLANN).
Eliminated the need for a hidden layer by expanding input patterns using Chebyshev polynomials.
Evaluated the FLANN for nonlinear dynamic system identification tasks.

Main Results:

The proposed FLANN significantly reduces computational requirements compared to MLPs.
FLANN performance is similar or superior to MLPs in the presence of additive Gaussian noise.
Demonstrated effectiveness in nonlinear dynamic system identification.

Conclusions:

The functional-link artificial neural network (FLANN) provides a computationally efficient solution for dynamic nonlinear system identification.
FLANN offers a viable alternative to traditional MLPs, especially when computational resources are limited.
The proposed network effectively handles noisy data in system identification problems.