Probabilistic Principal Components Analysis. [VAR, U, LAMBDA] = PPCA(X, PPCA_DIM) computes the principal ％ component subspace U of dimension PPCA_DIM using a centred covariance matrix X. The variable VAR contains the off-subspace variance (which is assumed to be spherical), while the vector LAMBDA contains the variances of each of the principal components. This is computed using the eigenvalue and eigenvector decomposition of X.
標(biāo)簽： Probabilistic Components Principal Analysis
上傳時(shí)間： 2016-04-28
上傳用戶：qb1993225
ofdm信道特性 Channel transmission simulator Channel transmission simulator ％ ％ inputs: ％ sig2 - noise variance ％ Mt - number of Tx antennas ％ Mr - number of Rx antennas ％ x - vector of complex input symbols (for MIMO, this is a matrix, where each column ％ is the value of the antenna outputs at a single time instance) ％ H - frequency selective channel - represented in block-Toeplitz form for MIMO transmission ％ N - number of symbols transmitted in OFDM frame ％ ％ outputs: ％ y - vector of channel outputs (matrix for MIMO again, just like x matrix) ％ create noise vector sequence (each row is a different antenna, each column is a ％ different time index) note: noise is spatially and temporally white
標(biāo)簽： transmission simulator Channel inputs
上傳時(shí)間： 2016-07-22
上傳用戶：kelimu
包括使用修正Gram-Schmit算法實(shí)現(xiàn)QR分解，自編LU分解、利用冪法和反冪法計(jì)算矩陣最大和最小特征值的程序。例外附有使用這些算法的例子供參考。 QR decomposition algorithm based on modified Gram-Schmit LU decomposition algorithm algorithm used to find maximum and minimum eigenvalue based on power and inverse power method and some examples are also included.
標(biāo)簽： Gram-Schmit 正 分解 算法
上傳時(shí)間： 2016-09-07
上傳用戶：cooran
The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order
標(biāo)簽： identification considered features separati
上傳時(shí)間： 2016-09-20
上傳用戶：FreeSky
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(biāo)簽： approximation converters successive family
上傳時(shí)間： 2016-11-20
上傳用戶：libenshu01
This function calculates Akaike s final prediction error ％ estimate of the average generalization error. ％ ％ [FPE,deff,varest,H] = fpe(NetDef,W1,W2,PHI,Y,trparms) produces the ％ final prediction error estimate (fpe), the effective number of ％ weights in the network if the network has been trained with ％ weight decay, an estimate of the noise variance, and the Gauss-Newton ％ Hessian. ％
標(biāo)簽： generalization calculates prediction function
上傳時(shí)間： 2014-12-03
上傳用戶：maizezhen
This function calculates Akaike s final prediction error ％ estimate of the average generalization error for network ％ models generated by NNARX, NNOE, NNARMAX1+2, or their recursive ％ counterparts. ％ ％ [FPE,deff,varest,H] = nnfpe(method,NetDef,W1,W2,U,Y,NN,trparms,skip,Chat) ％ produces the final prediction error estimate (fpe), the effective number ％ of weights in the network if it has been trained with weight decay, ％ an estimate of the noise variance, and the Gauss-Newton Hessian. ％
標(biāo)簽： generalization calculates prediction function
上傳時(shí)間： 2016-12-27
上傳用戶：腳趾頭
The AVRcam source files were built using the WinAVR distribution (version 3.3.1 of GCC). I haven t tested other versions of GCC, but they should compile without too much difficulty. * The source files for the AVRcam had the author name and copyright information added back into them after the judging of the project, since it states in the competition rules that the author s name can not be present during their inspection. * The included source files are the ones that were submitted for the entry into the Circuit Cellar contest. I have continued to develop the AVRcam, and have added several new features (such as ignoring objects that aren t larger than a minimum size, removing tracked objects that overlap with each, and some general optimizations). If you are interested in the latest source, email me at john@jrobot.net * For more info about the AVRcam, check out http://www.jrobot.net John Orlando August 20, 2004
標(biāo)簽： distribution version AVRcam source
上傳時(shí)間： 2016-12-30
上傳用戶：GavinNeko
ucos2 is a file system for embedded applications which can be used on any media, for which you can provide basic hardware access functions. µ C/FS is a high performance library that has been optimized for minimum memory consumption in RAM and ROM, high speed and versatility. It is written in ANSI C and can be used on any CPU.
標(biāo)簽： which applications can for
上傳時(shí)間： 2017-01-04
上傳用戶：偷心的海盜
ucos2 is a file system for embedded applications which can be used on any media, for which you can provide basic hardware access functions. µ C/FS is a high performance library that has been optimized for minimum memory consumption in RAM and ROM, high speed and versatility. It is written in ANSI C and can be used on any CPU.
標(biāo)簽： which applications can for
上傳時(shí)間： 2017-01-04
上傳用戶：13517191407
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