jsp和xml。XML and JSP are two important tools available in producing a web application. This chapter examines the
potential of mixing these two technologies in order to enhance the capabilities of JSP. While this chapter will
cover many things about XML, this chapter will not attempt to teach XML. I ...
To find and output 11-999 between the number of m, it is to meet m, m2 and m3 are several palindrome. The so-called palindrome refers to the number of its symmetrical figures that the whole number, for example, 121,676,94249 and so on. To meet the above requirements, such as the number of m = 11, m2 ...
Inside the C++ Object Model
Inside the C++ Object Model focuses on the underlying mechanisms that support object-oriented programming within C++: constructor semantics, temporary generation, support for encapsulation, inheritance, and "the virtuals"-virtual functions and virtual inheritance. This bo ...
This document contains official rules of the 3D soccer simulation competition
at RoboCup 2006. While we will try to cover all cases, if unexpected
events do occur, the rule committee will seek input from the
participants and then make a decision. However, once the committee has
made a decision, that ...
I often need a simple function generator. Just to generate a certain frequency. After all the years I ve worked with electronics, I still haven t got me one. Even though I need it now and then, I just couldn t seem to justify the cost of one.
So, standard solution - build one yourself.
I designed a ...
The TMS320LF240xA and TMS320LC240xA devices, new members of the TMS320C24x generation of
digital signal processor (DSP) controllers, are part of the TMS320C2000 platform of fixed-point DSPs. The
240xA devices offer the enhanced TMS320 DSP architectural design of the C2xx core CP ...
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
...
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 , w ...
