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Implementation of adaptive filters based on neural networks for digital signal processing
(ХНУРЕ, 2025) Stepuro, O. V.
One approach that overcomes the limitations of traditional methods is the use of multi-layer neural network architectures such as MLP and RNN. These networks can automatically learn from input signals and adjust their parameters for optimal filtering. The network consists of three main components: an input layer, one or more hidden layers, and an output layer, which allows for nonlinear transformation of the signal. The network is trained using the backpropagation algorithm, which minimizes the error between the actual and desired output.