Lms algorithm

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  • Tuyển tập các báo cáo nghiên cứu khoa học ngành toán học được đăng trên tạp chí toán học quốc tế đề tài: Dereverberation and denoising based on generalized spectral subtraction by multi-channel LMS algorithm using a small-scale microphone array

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Performance Analysis of the Consensus-Based Distributed LMS Algorithm

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Step Size Bound of the Sequential Partial Update LMS Algorithm with Periodic Input Signals

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Dereverberation and denoising based on generalized spectral subtraction by multi-channel LMS algorithm using a small-scale microphone array

    pdf11p sting06 16-02-2012 23 3   Download

  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Online Estimation of Time-Varying Volatility Using a Continuous-Discrete LMS Algorithm

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article An Efficient Implementation of the Sign LMS Algorithm Using Block Floating Point Format

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Modified Clipped LMS Algorithm Mojtaba Lotfizad

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  • We present an iterative procedure to build a Chinese language model (LM). We segment Chinese text into words based on a word-based Chinese language model. However, the construction of a Chinese LM itself requires word boundaries. To get out of the chicken-and-egg problem, we propose an iterative procedure that alternates two operations: segmenting text into words and building an LM. Starting with an initial segmented corpus and an LM based upon it, we use a Viterbi-liek algorithm to segment another set of data. Then, we build an LM based on the second set and use the resulting LM to...

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  • Introduction to Adaptive Filters 18.1 18.2 18.3 18.4 18.5 What is an Adaptive Filter? The Adaptive Filtering Problem Filter Structures The Task of an Adaptive Filter Applications of Adaptive Filters System Identification • Inverse Modeling • Linear Prediction • Feedforward Control General Form of Adaptive FIR Algorithms • The MeanSquared Error Cost Function • The Wiener Solution • The Method of Steepest Descent • The LMS Algorithm • Other Stochastic Gradient Algorithms • Finite-Precision Effects and Other Implementation Issues • System Identification Example 18.

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  • Adaptive Filters • • • Adaptive structures The least mean squares (LMS) algorithm Programming examples for noise cancellation and system identification using C code Adaptive filters are best used in cases where signal conditions or system parameters are slowly changing and the filter is to be adjusted to compensate for this change. The least mean squares (LMS) criterion is a search algorithm that can be used to provide the strategy for adjusting the filter coefficients. Programming examples are included to give a basic intuitive understanding of adaptive filters. 7.

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  • Adaptive Filters Adaptive structures The least mean square (LMS) algorithm Programming examples using C and TMS320C3x code Adaptive filters are best used in cases where signal conditions or system parameters are slowly changing and the filter is to be adjusted to compensate for this change. The least mean square (LMS) criterion is a search algorithm that can be used to provide the strategy for adjusting the filter coefficients. Programming examples are included to give a basic intuitive understanding of adaptive filters. ...

    pdf28p doroxon 16-08-2010 41 6   Download

  • This book focuses primarily on speech recognition and the related tasks such as speech enhancement and modeling. This book comprises 3 sections and thirteen chapters written by eminent researchers from USA, Brazil, Australia, Saudi Arabia, Japan, Ireland, Taiwan, Mexico, Slovakia and India. Section 1 on speech recognition consists of seven chapters. Sections 2 and 3 on speech enhancement and speech modeling have three chapters each respectively to supplement section 1.

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  • Motivation and Example Adaptive Filter Structure Performance and Robustness Issues Error and Energy Measures Robust Adaptive Filtering Energy Bounds and Passivity Relations Min-Max Optimality of Adaptive Gradient Algorithms Comparison of LMS and RLS Algorithms Time-Domain Feedback Analysis Ali H. Sayed University of California, Los Angeles Markus Rupp Bell Laboratories Lucent Technologies Time-Domain Analysis • l2 −Stability and the Small Gain Condition • Energy Propagation in the Feedback Cascade • A Deterministic Convergence Analysis 20.10Filtered-Error Gradient Algorithms 20.

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  • The System Identification Framework for Adaptive IIR Filtering • Algorithms and Performance Issues • Some Preliminaries 23.2 The Equation Error Approach The LMS and LS Equation Error Algorithms • Instrumental Variable Algorithms • Equation Error Algorithms with Unit Norm Constraints Gradient-Descent Algorithms Based on Stability Theory • 23.3 The Output Error Approach Output Error Algorithms 23.4 Equation-Error/Output-Error Hybrids The Steiglitz-McBride Family of Algorithms Geoffrey A. Williamson Illinois Institute of Technology 23.5 Alternate Parametrizations 23.

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  • This paper presents a comparative study of five parameter estimation algorithms on four NLP tasks. Three of the five algorithms are well-known in the computational linguistics community: Maximum Entropy (ME) estimation with L2 regularization, the Averaged Perceptron (AP), and Boosting. We also investigate ME estimation with L1 regularization using a novel optimization algorithm, and BLasso, which is a version of Boosting with Lasso (L1) regularization. We first investigate all of our estimators on two re-ranking tasks: a parse selection task and a language model (LM) adaptation task. ...

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  • In this paper, we present an efficient query selection algorithm for the retrieval of web text data to augment a statistical language model (LM). The number of retrieved relevant documents is optimized with respect to the number of queries submitted. The querying scheme is applied in the domain of SMS text messages. Continuous speech recognition experiments are conducted on three languages: English, Spanish, and French. The web data is utilized for augmenting in-domain LMs in general and for adapting the LMs to a user-specific vocabulary. ...

    pdf9p bunthai_1 06-05-2013 10 1   Download

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