FREE CLICK & COLLECT SERVICE NOW AVAILABLE | SPEND €10 FOR FREE HOME DELIVERY

Adaptive filtering

by Alexander D. Poularikas | 30 October 2014
Synopsis
Adaptive filters are used in many diverse applications, appearing in everything from military instruments to cellphones and home appliances. Adaptive Filtering: Fundamentals of Least Mean Squares with MATLAB® covers the core concepts of this important field, focusing on a vital part of the statistical signal processing area-the least mean square (LMS) adaptive filter. This largely self-contained text: Discusses random variables, stochastic processes, vectors, matrices, determinants, discrete random signals, and probability distributions Explains how to find the eigenvalues and eigenvectors of a matrix and the properties of the error surfaces Explores the Wiener filter and its practical uses, details the steepest descent method, and develops the Newton's algorithm Addresses the basics of the LMS adaptive filter algorithm, considers LMS adaptive filter variants, and provides numerous examples Delivers a concise introduction to MATLAB®, supplying problems, computer experiments, and more than 110 functions and script files Featuring robust appendices complete with mathematical tables and formulas, Adaptive Filtering: Fundamentals of Least Mean Squares with MATLAB® clearly describes the key principles of adaptive filtering and effectively demonstrates how to apply them to solve real-world problems.
€106.39
319 Reward Points
Currently out of stock
Delivery in 5-7 Days
Eligible for free delivery

Any purchases for more than €10 are eligible for free delivery anywhere in the UK or Ireland!

Synopsis
Adaptive filters are used in many diverse applications, appearing in everything from military instruments to cellphones and home appliances. Adaptive Filtering: Fundamentals of Least Mean Squares with MATLAB® covers the core concepts of this important field, focusing on a vital part of the statistical signal processing area-the least mean square (LMS) adaptive filter. This largely self-contained text: Discusses random variables, stochastic processes, vectors, matrices, determinants, discrete random signals, and probability distributions Explains how to find the eigenvalues and eigenvectors of a matrix and the properties of the error surfaces Explores the Wiener filter and its practical uses, details the steepest descent method, and develops the Newton's algorithm Addresses the basics of the LMS adaptive filter algorithm, considers LMS adaptive filter variants, and provides numerous examples Delivers a concise introduction to MATLAB®, supplying problems, computer experiments, and more than 110 functions and script files Featuring robust appendices complete with mathematical tables and formulas, Adaptive Filtering: Fundamentals of Least Mean Squares with MATLAB® clearly describes the key principles of adaptive filtering and effectively demonstrates how to apply them to solve real-world problems.
€106.39
319 Reward Points
Currently out of stock
Delivery in 5-7 Days
Eligible for free delivery

Any purchases for more than €10 are eligible for free delivery anywhere in the UK or Ireland!


Product Details

ISBN - 9781482253351
Format -
Publisher -
Published - 30/10/2014
Categories - All, Books, Education, Engineering And Construction Academic
No. of Pages - 363
Weight - 540
Edition -
Series - - Not Available
Page Size - 24
Language - en-US
Readership Age - Not Available
Table of Contents - Not Available

Delivery And Returns

Please Note: Items in our extended range may take longer to deliver. For this reason, we cannot guarantee delivery of this item before Christmas.

Place an order for over €10 to receive free delivery to anywhere in Ireland and the UK! See our Delivery Charges section below for a full breakdown of shipping costs for all destinations.

 

Delivery Charges

  Ireland & UK* Europe & USA Australia & Canada Rest of World
Under €10 €3.80 €10 €15 €25
Over €10
Free €10 €15 €25

*Free delivery on all orders over €10 - only applies to order total.

All orders will be delivered by An Post.