gps matlab 仿真程序（A collection of geodetic functions that solve a variety of problems in geodesy. Supports a wide range of common and user-defIned reference ellipsoids. Most functions are vectorized.）
標(biāo)簽： collection functions geodetic problems
上傳時(shí)間： 2014-12-07
上傳用戶：whenfly
This GUI can be used by entering nu at the MATLAB command prompt. The user can either select a function (f(x)) of their choice or a statistical distribution probability distribution function to plot over a user defIned range. The function s integral can be evaluated over a user defIned range by using: The composite trapezium, simpsons and gauss-legendre rules. This is useful for calculating accurate probabilities that one might see in statistical tables.
標(biāo)簽： can entering command MATLAB
上傳時(shí)間： 2016-11-07
上傳用戶：偷心的海盜
neural network utility is a Neural Networks library for the C++ Programmer. It is entirely object oriented and focuses on reducing tedious and confusing problems of programming neural networks. By this I mean that network layers are easily defIned. An entire multi-layer network can be created in a few lines, and trained with two functions. Layers can be connected to one another easily and painlessly.
標(biāo)簽： Programmer Networks entirely network
上傳時(shí)間： 2013-12-24
上傳用戶：liuchee
Batch version of the back-propagation algorithm. ％ Given a set of corresponding input-output pairs and an initial network ％ [W1,W2,critvec,iter]=batbp(NetDef,W1,W2,PHI,Y,trparms) trains the ％ network with backpropagation. ％ ％ The activation functions must be either linear or tanh. The network ％ architecture is defIned by the matrix NetDef consisting of two ％ rows. The first row specifies the hidden layer while the second ％ specifies the output layer. ％
標(biāo)簽： back-propagation corresponding input-output algorithm
上傳時(shí)間： 2016-12-27
上傳用戶：exxxds
％ Train a two layer neural network with the Levenberg-Marquardt ％ method. ％ ％ If desired, it is possible to use regularization by ％ weight decay. Also pruned (ie. not fully connected) networks can ％ be trained. ％ ％ Given a set of corresponding input-output pairs and an initial ％ network, ％ [W1,W2,critvec,iteration,lambda]=marq(NetDef,W1,W2,PHI,Y,trparms) ％ trains the network with the Levenberg-Marquardt method. ％ ％ The activation functions can be either linear or tanh. The ％ network architecture is defIned by the matrix NetDef which ％ has two rows. The first row specifies the hidden layer and the ％ second row specifies the output layer.
標(biāo)簽： Levenberg-Marquardt desired network neural
上傳時(shí)間： 2016-12-27
上傳用戶：jcljkh
Train a two layer neural network with a recursive prediction error ％ algorithm ("recursive Gauss-Newton"). Also pruned (i.e., not fully ％ connected) networks can be trained. ％ ％ The activation functions can either be linear or tanh. The network ％ architecture is defIned by the matrix NetDef , which has of two ％ rows. The first row specifies the hidden layer while the second ％ specifies the output layer.
標(biāo)簽： recursive prediction algorithm Gauss-Ne
上傳時(shí)間： 2016-12-27
上傳用戶：ljt101007
Quartz is a full-featured, open source job scheduling system that can be integrated with, or used along side virtually any J2EE or J2SE application - from the smallest stand-alone application to the largest e-commerce system. Quartz can be used to create simple or complex schedules for executing tens, hundreds, or even tens-of-thousands of jobs jobs whose tasks are defIned as standard Java components or EJBs. The Quartz Scheduler includes many enterprise-class features, such as JTA transactions and clustering.
標(biāo)簽： full-featured integrated scheduling Quartz
上傳時(shí)間： 2014-01-07
上傳用戶：龍飛艇
Excel的強(qiáng)大函數(shù)功能，為我們帶來了極大方便，雖然它有200多個(gè)函數(shù)，但有時(shí)我們?yōu)閷?shí)現(xiàn)一項(xiàng)規(guī)定的功能，可能用到幾個(gè)函數(shù)或者采用嵌套函數(shù)，這樣應(yīng)用起來，也不太方便。其實(shí)對于我們經(jīng)常用到的功能，我們可以創(chuàng)建自定義函數(shù)UDF(User—defIned function)，它的運(yùn)行與Excel中自帶的函數(shù)完全相同。建立自定義函數(shù)有以下幾個(gè)優(yōu)點(diǎn)：建立自己特殊的功能和名稱的函數(shù)；能將復(fù)雜的、嵌套的、多個(gè)原有的函數(shù)組合在一起，發(fā)揮更大的威力。
上傳時(shí)間： 2013-12-24
上傳用戶：gxrui1991
The Software Engineering Institute’s (SEI) Capability Maturity Model (CMM) provides a well-known benchmark of software process maturity. The CMM has become a popular vehicle for assessing the maturity of an organization’s software process in many domains. This white paper describes how the Rational Unified Process can support an organization that is trying to achieve CMM Level-2, Repeatable, and Level-3, defIned, software process maturity levels.
標(biāo)簽： Engineering Capability well-known Institute
上傳時(shí)間： 2017-02-27
上傳用戶：zhichenglu
Measuring Frequency Content in Signals I this section we will study some non parametric methods for spectrum estimation of a stochastic process. These methods are described in the literature. All methods are based on the Periodogram which is defIned for a sequence x[n] with length N according to
標(biāo)簽： parametric Measuring Frequency Content
上傳時(shí)間： 2017-03-20
上傳用戶：秦莞爾w
蟲蟲下載站版權(quán)所有 京ICP備2021023401號(hào)-1
