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(biāo)簽： algorithms LMS algorithm filtering
上傳時間： 2014-01-23
上傳用戶：yxgi5
分析for i=E STEP E until E do i=E
上傳時間： 2013-12-03
上傳用戶：GHF
Implement the STEP 2 of two-level logic minimization. Our goal is to find the minimum (exact minimum) sum-of-products expression for a given function.
標(biāo)簽： minimization Implement the two-level
上傳時間： 2014-01-09
上傳用戶：無聊來刷下
This the mathematical computational method of STEP-vary Gill method.
標(biāo)簽： method computational mathematical STEP-vary
上傳時間： 2014-01-02
上傳用戶：lili123
windows ce .net STEP-by-STEP
標(biāo)簽： STEP-by-STEP windows net ce
上傳時間： 2015-12-31
上傳用戶：頂?shù)弥?/p>
for total beginner to learn Eclipse and Java ,STEP by STEP teach you how to develop java pragams under eclipse IDE
標(biāo)簽： STEP beginner Eclipse develop
上傳時間： 2013-12-17
上傳用戶：yoleeson
很全的中斷手冊。 INT 00 - CPU-generated - DIVIDE ERROR INT 01 - CPU-generated - SINGLE STEP (80386+) - DEBUGGING EXCEPTIONS INT 02 - external hardware - NON-MASKABLE INTERRUPT INT 03 - CPU-generated - BREAKPOINT INT 04 - CPU-generated - INTO DETECTED OVERFLOW INT 05 - PRINT SCREEN CPU-generated (80186+) - BOUND RANGE EXCEEDED INT 06 - CPU-generated (80286+) - INVALID OPCODE INT 07 - CPU-generated (80286+) - PROCESSOR EXTENSION NOT AVAILABLE INT 08 - IRQ0 - SYSTEM TIMER CPU-generated (80286+) . . .
標(biāo)簽： CPU-generated INT DIVIDE SINGLE
上傳時間： 2013-12-27
上傳用戶：aa54
原書名study arm STEP by STEP 想學(xué)ARM的 可以用這個起步
上傳時間： 2013-12-12
上傳用戶：wys0120
STEP by STEP移植LCD驅(qū)動，這是一個很好的說明文檔，適用于初學(xué)者
上傳時間： 2013-12-23
上傳用戶：hullow
How the K-mean Cluster work STEP 1. Begin with a decision the value of k = number of clusters STEP 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. STEP 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. STEP 4 . Repeat STEP 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.
標(biāo)簽： the decision clusters Cluster
上傳時間： 2013-12-21
上傳用戶：gxmm
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