Abstract: The brief proposes a radial basis function (RBF) neural network (NN)-enabled adaptive filter (AF) algorithm, which consists of two stages. The first stage is a data-driven (DD) preprocessing ...
We are very welcome to your contribution. Please feel free to reach out to us with your feedback and suggestions on how to improve the current models. This public repository contains Python (Jupyter) ...
Radial Basis Function Neural Networks (RBFNNs) are a type of neural network that combines elements of clustering and function approximation, making them powerful for both regression and classification ...
Continual learning is a rapidly evolving area of research that focuses on developing models capable of learning from sequentially arriving data streams, similar to human learning. It addresses the ...
ABSTRACT: Accurately approximating higher order derivatives is an inherently difficult problem. It is shown that a random variable shape parameter strategy can improve the accuracy of approximating ...
Accurately approximating higher order derivatives is an inherently difficult problem. It is shown that a random variable shape parameter strategy can improve the accuracy of approximating higher order ...
Clinically deployed deep brain stimulation (DBS) for the treatment of Parkinson’s disease operates in an open loop with fixed stimulation parameters, and this may result in high energy consumption and ...
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