Chebyshev polynomials-based (CPB) unified model neural networks for function approximation
Tsu‐Tian Lee, Jin-Tsong Jeng · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1997
In this paper, we propose the approximate transformable technique, which includes the direct transformation and indirect transformation, to obtain a CPB unified model neural networks for feedforward/recurrent neural networks via Chebyshev polynomials approximation. Based on this approximate transformable technique, we have derived the relationship between the single-layer neural networks and multilayer perceptron neural networks. It is shown that the CPB unified model neural networks can be represented as a functional link networks that are based on Chebyshev polynomials, and these networks use the recursive least squares method with forgetting factor as learning algorithm. It turns out that the CPB unified model neural networks not only has the same capability of universal approximator, but also has faster learning speed than conventional feedforward/recurrent neural networks. Computer simulations show that the proposed method does have the capability of universal approximator in some functional approximation with considerable reduction in learning time.