Digital Filters Optimization Modelling with Non-canonical Hypercomplex Number Systems
Recursive digital filter modelling is one of the tasks, which modelling can be improved by using hypercomplex numbers. Existing models are about data representation in canonical hypercomplex number system only. However, canonical number systems have some restrictions. Applying the non-canonical number systems gives more possibilities for filter simulation and its further optimization by its parametric sensitivity since they have more structure constants in Keli table.
The paper proposes a digital filter synthesis method, which is using non-canonical hypercomplex number systems. Use of non-canonical hypercomplex number system with greater number of non-zero structure constants in Keli table can significantly improve the sensitivity of the digital filter.
KeywordsHypercomplex numbers Non-canonical number system Digital filter sensitivity Filter sensitivity
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