Abstract
The models of multi-input pulse neuron in generalized vector-matrix form are proposed in order to solve digital signal processing problems. Nonrecursive and recursive digital models are considered. Nonrecursive models use the description of linear systems in a convolution form and the input signal is presented as a sequential or a parallel binary vector. Recursive models are based on the description of linear systems in the time domain and use an impulse response and a state space approaches. A learning rule for the mentioned models of a pulse neuron is derived to solve a problem of signal reconstruction and adaptive noise suppression. Results of a computer simulation are presented.
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Bondarev, V. (2016). Vector-Matrix Models of Pulse Neuron for Digital Signal Processing. In: Cheng, L., Liu, Q., Ronzhin, A. (eds) Advances in Neural Networks – ISNN 2016. ISNN 2016. Lecture Notes in Computer Science(), vol 9719. Springer, Cham. https://doi.org/10.1007/978-3-319-40663-3_74
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DOI: https://doi.org/10.1007/978-3-319-40663-3_74
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