The Chebyshev-polynomials-based unified model neural networks for function approximation
β Scribed by Tsu-Tian Lee; Jin-Tsong Jeng
- Book ID
- 117874463
- Publisher
- IEEE
- Year
- 1998
- Tongue
- English
- Weight
- 400 KB
- Volume
- 28
- Category
- Article
- ISSN
- 1083-4419
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π SIMILAR VOLUMES
In this paper, we give a universal approach to approximation of non-linear functionals and so called myopic input-output maps by neural network-like architectures. Strong theorems on equi-uniform approximation to functionals in abstract spaces are given. As applications, theorems on identification o
Two problems occur in the design of feedforward neural networks: the choice of the optimal architecture and the initialization. Generally, input and output data of a system (or a function) are measured and recorded. Then, experimenters wish to design a neural network to map exactly these output valu