對圖像進行l(wèi)ocal APPROXIMATION處理的技術,應用于圖像去模糊中.
標簽： APPROXIMATION local 圖像 應用于
上傳時間： 2014-12-01
上傳用戶：daoxiang126
The ADC0803 family is a series of three CMOS 8-bit successive APPROXIMATION A/D converters using a resistive ladder and capacitive array together with an auto-zero comparator. These converters are designed to operate with microprocessor-controlled buses using a minimum of external circuitry. The 3-State output data lines can be connected directly to the data bus.
標簽： APPROXIMATION converters successive family
上傳時間： 2016-11-20
上傳用戶：libenshu01
MATLAB CODE FOR SIMULATION OF A SUCCESSIVE APPROXIMATION CONVERTER
標簽： APPROXIMATION SIMULATION SUCCESSIVE CONVERTER
上傳時間： 2017-01-15
上傳用戶：問題問題
meshless method programme for moving least square APPROXIMATION
標簽： APPROXIMATION programme meshless method
上傳時間： 2014-01-23
上傳用戶：jkhjkh1982
The most straightforward APPROXIMATION is the standard Gaussian APPROXIMATION, where the MAI is approximated by a Gaussian random variable. This APPROXIMATION is simple, however it is not accurate in general. In situations where the number of users is not large, the Gaussian APPROXIMATION is not appropriate. In-depth analysis of must be applied. The Holtzman?s improved Gaussian APPROXIMATION provides a better APPROXIMATION to the MAI term. The APPROXIMATION conditions the interference term on the operation condition of each user.
標簽： APPROXIMATION straightforward the Gaussian
上傳時間： 2017-06-03
上傳用戶：dyctj
Solve TSP by local APPROXIMATION
標簽： APPROXIMATION Solve local TSP
上傳時間： 2017-07-06
上傳用戶：遠遠ssad
A Deflationary fastICA using negentropy APPROXIMATION implementted in Matlab
標簽： APPROXIMATION Deflationary implementted negentropy
上傳時間： 2017-07-11
上傳用戶：star_in_rain
right APPROXIMATION of integrals
標簽： APPROXIMATION integrals right of
上傳時間： 2017-09-15
上傳用戶：anng
APPROXIMATION algorithm
上傳時間： 2021-09-04
上傳用戶：dqq1019
自適應(Adaptive)神經網(wǎng)絡源程序 The adaptive Neural Network Library is a collection of blocks that implement several Adaptive Neural Networks featuring different adaptation algorithms.~..~ There are 11 blocks that implement basically these 5 kinds of neural networks: 1) Adaptive Linear Network (ADALINE) 2) Multilayer Layer Perceptron with Extended Backpropagation algorithm (EBPA) 3) Radial Basis Functions (RBF) Networks 4) RBF Networks with Extended Minimal Resource Allocating algorithm (EMRAN) 5) RBF and Piecewise Linear Networks with Dynamic Cell Structure (DCS) algorithm A simulink example regarding the APPROXIMATION of a scalar nonlinear function of 4 variables
標簽： collection implement Adaptive adaptive
上傳時間： 2015-04-09
上傳用戶：ywqaxiwang
