Abstract
This paper presents a concept of the non-stationary filtering network with reduced transient response consisting of the first-order digital elements with time-varying parameters. The digital filter section is based on the analog system. In order to design the filtering network, the analog prototype was subjected to the discretization process. The time constant and the gain factor were then temporarily varied in time in order to suppress the transient response of the designed filtering structure. The optimization method, based on the Particle Swarm Optimization (PSO) algorithm which is aimed at reducing the settling time by a proper parameter manipulation, is presented. Simulation results proving the usefulness of the proposed concept are also shown and discussed.
Highlights
Nowadays, digital and analog filters are both commonly used for analysis and signal processing.Their effectiveness and quick operation allow them to eliminate unwanted noise or signal components.Many of their designs, implementation methods, and programming are well known [1–4]
This paper proposes a new method of selecting optimal parameters of the functions that vary the filter coefficients, where all necessary values are specified by the chosen algorithm, in this case the Particle Swarm Optimization (PSO) algorithm [5–7]
The results of the preceding research [13–15] have shown that filters with time-varying parameters work undoubtedly better in the time domain than filters with time-invariant parameters
Summary
Digital and analog filters are both commonly used for analysis and signal processing. Their effectiveness and quick operation allow them to eliminate unwanted noise or signal components. The final structure is defined by weights Their value affects which signal is forwarded to the layer of the network. A network composed of these non-stationary elements has the ability to shape frequency characteristics without any restraint, and is characterized by proper accuracy of the filtering process. This allows for the possibility of expansion which enables operation with even the most complex signals.
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