Abstract
Abstract This paper introduces an innovative interpretation of spiking signal processing (SSP) and proposes applications in control system. Taking the firing rate as the coding principle employed by biological neurons, we have transformed continuous signals in geometric spiking series evolving over time. New fundamental SSP equations are presented and compared to classical digital signal processing (DSP). Application to the linear system analysis and control drive using spiking transformation is finally presented. This paper launches the theoretical SSP basis to investigate more deeply the geometric neural networks with learning ability.
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