Adaptive Filtering: Fundamentals of Least Mean Squares with Matlab(r)
暫譯: 自適應濾波：最小均方誤差的基本原理與 Matlab(r)
Poularikas, Alexander D.
商品描述
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.
商品描述(中文翻譯)
自適應濾波器被廣泛應用於各種不同的領域，從軍事儀器到手機和家用電器皆有其身影。《自適應濾波：最小均方誤差的基本原理與 MATLAB》涵蓋了這一重要領域的核心概念，專注於統計信號處理領域中的一個重要部分——最小均方誤差（LMS）自適應濾波器。
這本基本上自成一體的書籍：
討論隨機變數、隨機過程、向量、矩陣、行列式、離散隨機信號和機率分佈
解釋如何找到矩陣的特徵值和特徵向量以及誤差表面的性質
探討維納濾波器及其實際應用，詳細介紹最速下降法，並發展牛頓算法
介紹LMS自適應濾波器算法的基本概念，考慮LMS自適應濾波器的變體，並提供大量範例
提供 MATLAB 的簡明介紹，附有問題、計算機實驗以及超過110個函數和腳本文件
《自適應濾波：最小均方誤差的基本原理與 MATLAB》附有完整的數學表和公式的強大附錄，清楚地描述了自適應濾波的關鍵原則，並有效地展示了如何將其應用於解決現實世界中的問題。
