Description
T and F CRC Nonlinear Digital Filtering With Python: An Introduction by Ronald K Pearson
Nonlinear Digital Filtering with Python: An Introduction discusses important structural filter classes including the median filter and a number of its extensions (e.g., weighted and recursive median filters), and Volterra filters based on polynomial nonlinearities. Adopting both structural and behavioral approaches in characterizing and designing nonlinear digital filters. Nonlinear Digital Filtering with Python: An Introduction supplies essential knowledge useful for developing and implementing data cleaning filters for dynamic data analysis and time-series modeling.
Key Features:-
- Begins with an expedient introduction to programming in the free, open-source computing environment
- Uses results from algebra and the theory of functional equations to construct and characterize behav
- Analyzes the impact of a range of useful interconnection strategies on filter behavior, providing Py
- Proposes practical, bottom-up strategies for designing more complex and capable filters from simpler
- Illustrates the behavioral consequences of allowing recursive (i.e., feedback) interconnections in n