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. ％
標簽： generalization calculates prediction function
上傳時間： 2016-12-27
上傳用戶：腳趾頭
This function applies the Optimal Brain Surgeon (OBS) strategy for ％ pruning neural network models of dynamic systems. That is networks ％ trained by NNARX, NNOE, NNARMAX1, NNARMAX2, or their recursive ％ counterparts.
標簽： function strategy Optimal Surgeon
上傳時間： 2013-12-19
上傳用戶：ma1301115706
Please carefully read the many features of your package and then write the specific function (at least 20 words). As far as possible not to let the station master of the time spent in the
標簽： carefully the features function
上傳時間： 2013-12-16
上傳用戶：ouyangtongze
The Kalman filter is a set of mathematical equations that provides an efficient computational [recursive] means to estimate the state of a process, in a way that minimizes the mean of the squared error. The filter is very powerful in several aspects: it supports estimations of past, present, and even future states, and it can do so even when the precise nature of the modeled system is unknown.
標簽： computational mathematical equations efficient
上傳時間： 2014-06-02
上傳用戶：yd19890720
In computer vision, sets of data acquired by sampling the same scene or object at different times, or from different perspectives, will be in different coordinate systems. Image registration is the process of transforming the different sets of data into one coordinate system. Registration is necessary in order to be able to compare or integrate the data obtained from different measurements. Image registration is the process of transforming the different sets of data into one coordinate system. To be precise it involves finding transformations that relate spatial information conveyed in one image to that in another or in physical space. Image registration is performed on a series of at least two images, where one of these images is the reference image to which all the others will be registered. The other images are referred to as target images.
標簽： different computer acquired sampling
上傳時間： 2013-12-28
上傳用戶：來茴
In some graphs, the shortest path is given by optimizing two different metrics: the sum of weights of the edges and the number of edges. For example: if two paths with equal cost exist then, the path with the least number of edges is chosen as the shortest path. Given this metric, you have find out the shortest path between a given pair of vertices in the input graph. The output should be the number of edges on the path, the cost of the shortest path, and the path itself. Input is the adjacency matrix and the two vertices.
標簽： optimizing different the shortest
上傳時間： 2014-10-25
上傳用戶：1159797854
he basic idea of the method of bisection is to start with an initial interval, [a0,b0], that is chosen so that f(a0)f(b0) < 0. (This guarantees that there is at least one root of the function f(x) within the initial interval.) We then iteratively bisect the interval, generating a sequence of intervals [ak,bk] that is guaranteed to converge to a solution to f(x) = 0.
標簽： bisection interval initial method
上傳時間： 2017-04-29
上傳用戶：zsjinju
伸展樹，基本數據結構，The tree is drawn in such a way that both of the edges down from a node are the same length. This length is the minimum such that the two subtrees are separated by at least two blanks.
標簽： 樹
上傳時間： 2017-05-07
上傳用戶：JIUSHICHEN
Novell.Press.Linux.Kernel.Development linux內核開發的經典書籍之一 The Linux kernel is one of the most interesting yet least understood open-source projects. It is also a basis for developing new kernel code. That is why Sams is excited to bring you the latest Linux kernel development information from a Novell insider in the second edition of Linux Kernel Development. This authoritative, practical guide will help you better understand the Linux kernel through updated coverage of all the major subsystems, new features associated with Linux 2.6 kernel and insider information on not-yet-released developments. You ll be able to take an in-depth look at Linux kernel from both a theoretical and an applied perspective as you cover a wide range of topics, including algorithms, system call interface, paging strategies and kernel synchronization. Get the top information right from the source in Linux Kernel Development
標簽： Linux Development interes Novell
上傳時間： 2017-06-06
上傳用戶：songyue1991
This is the project README file. Here, you should describe your project. Tell the reader (someone who does not know anything about this project) all he/she needs to know. The comments should usually include at least:
標簽： project the describe someone
上傳時間： 2017-06-22
上傳用戶：450976175
