Nonlinear Digital Filtering with Python

Name: Nonlinear Digital Filtering with Python
Brand: Taylor & Francis Inc
SKU: 9781498714112
Price: 177.41 EUR
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An Introduction

Автор: Moncef Gabbouj

Език:

Корици: С твърди корици

Издател: Taylor & Francis Inc

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Nonlinear Digital Filtering with Python: An Introduction discusses important structural filter class...

Информация за книгата

Автор

Moncef Gabbouj

Език

Английски език

Корици

Книга - С твърди корици

Издадена

2015

страници

286

EAN

9781498714112

ISBN

9781498714112

Enbook ID

09407283

Издател

Taylor & Francis Inc

Теглоt

630

Размери

163 x 243 x 23

Пълно описание

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, this book:

Begins with an expedient introduction to programming in the free, open-source computing environment of Python
Uses results from algebra and the theory of functional equations to construct and characterize behaviorally defined nonlinear filter classes
Analyzes the impact of a range of useful interconnection strategies on filter behavior, providing Python implementations of the presented filters and interconnection strategies
Proposes practical, bottom-up strategies for designing more complex and capable filters from simpler components in a way that preserves the key properties of these components
Illustrates the behavioral consequences of allowing recursive (i.e., feedback) interconnections in nonlinear digital filters while highlighting a challenging but promising research frontier

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.

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Nonlinear Digital Filtering with Python

An Introduction

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