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Proceedings Paper

Complex Chebyshev-polynomial-based unified model (CCPBUM) neural networks
Author(s): Jin-Tsong Jeng; Tsu-Tian Lee
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Paper Abstract

In this paper, we propose complex Chebyshev Polynomial Based unified model neural network for the approximation of complex- valued function. Based on this approximate transformable technique, we have derived the relationship between the single-layered neural network and multi-layered perceptron neural network. It is shown that the complex Chebyshev Polynomial Based unified model neural network can be represented as a functional link network that are based on Chebyshev polynomial. We also derived a new learning algorithm for the proposed network. It turns out that the complex Chebyshev Polynomial Based unified model neural network not only has the same capability of universal approximator, but also has faster learning speed than conventional complex feedforward/recurrent neural network.

Paper Details

Date Published: 25 March 1998
PDF: 9 pages
Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998); doi: 10.1117/12.304844
Show Author Affiliations
Jin-Tsong Jeng, Hwa-Hsia Junior College of Technology (Taiwan)
Tsu-Tian Lee, National Taiwan Institute of Technology (Taiwan)


Published in SPIE Proceedings Vol. 3390:
Applications and Science of Computational Intelligence
Steven K. Rogers; David B. Fogel; James C. Bezdek; Bruno Bosacchi, Editor(s)

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