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.
In this paper, the authors propose a neural network model with a fast learning speed as well as a good function approximation capability, ...
It turns out that the Chebyshev-Polynomials-Based CPB unified model neural networks not only has the same capability of universal approximator, ...
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In this paper, we propose a neural network model with a faster learning speed and a good approximate capability in the function approximation for solving ...
The unknown system nonlinearity is approximated by Chebyshev neural network (CNN), and weight update law is presented for approximating the system nonlinearity.
Jun 14, 2024 · This paper presents the Chebyshev Kolmogorov-Arnold Network (Chebyshev KAN), a new neural network architecture inspired by the Kolmogorov-Arnold representation ...
[PDF] Chebyshev Polynomial-Based Kolmogorov-Arnold Networks
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May 12, 2024 · A new neural network architecture inspired by the Kolmogorov-Arnold representation theorem, incorporating the powerful approximation capabilities of Chebyshev ...
This paper introduces a polynomial feedforward neural network based on Chebyshev polynomials able to effectively model non-linear and highly complex ...
Abstract: An alternative polynomial approximation for the activation sigmoid function is developed here. It can considerably simplify the input/output opera-.
Jun 14, 2024 · This paper presents the Chebyshev Kolmogorov-Arnold Network (Chebyshev KAN), a new neural network architecture inspired by the Kolmogorov-Arnold ...