Some algorithms of variable step size LMS adaptive filtering are studied．The VS—LMS algorithm is improved． Another new non-LINEar function between肛and e(／ t)is established．The theoretic analysis and computer simulation results show that this algorithm converges more quickly than the origina1．Furthermore，better antinoise property is exhibited under Low—SNR environment than the original one．
標簽： algorithms LMS algorithm filtering
上傳時間： 2014-01-23
上傳用戶：yxgi5
In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discrete-data LINEar filtering problem. Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and application, particularly in the area of autonomous or assisted navigation.
標簽： R.E. discrete-dat describing published
上傳時間： 2015-10-22
上傳用戶：2404
In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discretedata LINEar filtering problem [Kalman60]. Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and application, particularly in the area of autonomous or assisted navigation. A very “friendly” introduction to the general idea of the Kalman filter can be found in Chapter 1 of [Maybeck79], while a more complete introductory discussion can be found in [Sorenson70], which also contains some interesting historical narrative.
標簽： R.E. discretedata describing published
上傳時間： 2015-10-22
上傳用戶：a673761058
his paper provides a tutorial and survey of methods for parameterizing surfaces with a view to applications in geometric modelling and computer graphics. We gather various concepts from di® erential geometry which are relevant to surface mapping and use them to understand the strengths and weaknesses of the many methods for parameterizing piecewise LINEar surfaces and their relationship to one another.
標簽： parameterizing provides tutorial surfaces
上傳時間： 2014-11-09
上傳用戶：努力努力再努力
A one-dimensional calibration object consists of three or more colLINEar points with known relative positions. It is generally believed that a camera can be calibrated only when a 1D calibration object is in planar motion or rotates around a ¯ xed point. In this paper, it is proved that when a multi-camera is observing a 1D object undergoing general rigid motions synchronously, the camera set can be LINEarly calibrated. A LINEar algorithm for the camera set calibration is proposed，and then the LINEar estimation is further re¯ ned using the maximum likelihood criteria. The simulated and real image experiments show that the proposed algorithm is valid and robust.
標簽： one-dimensional calibration colLINEar consists
上傳時間： 2014-01-12
上傳用戶：璇珠官人
lms算法實現單波束形成，線陣單波束形成，(LINEar array signal beamforming )
上傳時間： 2015-11-19
上傳用戶：skfreeman
ITU-T G.729語音壓縮算法。 description: Fixed-point description of commendation G.729 with ANNEX B Coding of Speech at 8 kbit/s using Conjugate-Structure Algebraic-Code-Excited LINEar-Prediction (CS-ACELP) with Voice Activity Decision(VAD), Discontinuous Transmission(DTX), and Comfort Noise Generation(CNG).
標簽： description commendation Fixed-point 729
上傳時間： 2014-11-23
上傳用戶：thesk123
This paper examines the asymptotic (large sample) performance of a family of non-data aided feedforward (NDA FF) nonLINEar least-squares (NLS) type carrier frequency estimators for burst-mode phase shift keying (PSK) modulations transmitted through AWGN and flat Ricean-fading channels. The asymptotic performance of these estimators is established in closed-form expression and compared with the modified Cram`er-Rao bound (MCRB). A best LINEar unbiased estimator (BLUE), which exhibits the lowest asymptotic variance within the family of NDA FF NLS-type estimators, is also proposed.
標簽： performance asymptotic examines non-data
上傳時間： 2015-12-30
上傳用戶：225588
The need for accurate monitoring and analysis of sequential data arises in many scientic, industrial and nancial problems. Although the Kalman lter is effective in the LINEar-Gaussian case, new methods of dealing with sequential data are required with non-standard models. Recently, there has been renewed interest in simulation-based techniques. The basic idea behind these techniques is that the current state of knowledge is encapsulated in a representative sample from the appropriate posterior distribution. As time goes on, the sample evolves and adapts recursively in accordance with newly acquired data. We give a critical review of recent developments, by reference to oil well monitoring, ion channel monitoring and tracking problems, and propose some alternative algorithms that avoid the weaknesses of the current methods.
標簽： monitoring sequential industria accurate
上傳時間： 2013-12-17
上傳用戶：familiarsmile
Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a LINEar combination of classifiers by overweighting the examples that are misclassified by each classifier. icsiboost implements Adaboost over stumps (one-level decision trees) on discrete and continuous attributes (words and real values). See http://en.wikipedia.org/wiki/AdaBoost and the papers by Y. Freund and R. Schapire for more details [1]. This approach is one of most efficient and simple to combine continuous and nominal values. Our implementation is aimed at allowing training from millions of examples by hundreds of features in a reasonable time/memory.
標簽： meta-learning classifiers combining Boosting
上傳時間： 2016-01-30
上傳用戶：songnanhua
