This program demonstrates some function approximation capabilities of a Radial Basis Function Network.
The user supplies a set of training points which represent some "sample" points for some arbitrary curve. Next, the user specifies the number of equally spaced gaussian centers and the variance for the network. Using the training samples, the weights multiplying each of the gaussian basis functions arecalculated using the pseudo-inverse (yielding the minimum least-squares solution). The resulting network is then used to approximate the function between the given "sample" points.
Predefined Style options define the style by setting several other options. If other options are also used, the placement of the predefined style option in the command line is important. If the predefined style option is placed first, the other options may override the predefined style. If placed last, the predefined style will override the other options.
For example the style --style=ansi sets the option --brackets=break . If the command line specifies "--style=ansi --brackets=attach", the brackets will be attached and the style will not be ansi style. If the order on the command line is reversed to "--brackets=attach --style=ansi ", the brackets will be broken (ansi style) and the attach option will be ignored.
For the options set by each style check the parseOption function in astyle_main.cpp
The Joint Video Team (JVT) of ISO/IEC MPEG and ITU-T VCEG are finalising a new standard for
the coding (compression) of natural video images. The new standard [1] will be known as H.264 and
also MPEG-4 Part 10, “Advanced Video Coding”. The standard specifies two types of entropy coding:
Context-based Adaptive Binary Arithmetic Coding (CABAC) and Variable-Length Coding (VLC).
This document provides a short introduction to CABAC. Familiarity with the concept of Arithmetic
Coding is assumed.
The Joint Video Team (JVT) of ISO/IEC MPEG and ITU-T VCEG are finalising a new standard for
the coding (compression) of natural video images. The new standard [1] will be known as H.264 and
also MPEG-4 Part 10, “Advanced Video Coding”. The standard specifies two types of entropy coding:
Context-based Adaptive Binary Arithmetic Coding (CABAC) and Variable-Length Coding (VLC).
The Variable-Length Coding scheme, part of the Baseline Profile of H.264, is described in this
document.
IEEE Std 1180-1990. IEEE Standard Specifications for the Implementations of 8x8 Inverse Discrete Cosine Transform, specifies the numerical characteristics of the 8x8 inverse discrete cosine transform (IDCT) for use in visual telephony and similar applications where the 8x8 IDCT results are used in a reconstruction loop. The specifications ensure the compatibility between different implementations of the IDCT.
CANopen is a networking system based on the CAN serial bus.
CANopen assumes that the device’s hardware has a CAN transceiver
and CAN controller as specified in ISO 11898.
CANopen profile family specifies standardized communication
mechanisms and device functionality. The profile family is available
and maintained by CAN in Automation (CiA), the international users’
and manufacturers’ group and may be implemented license-free.
javascript中文教程
<INPUT
TYPE="button"
NAME="objectName"
VALUE="buttonText"
[onClick="handlerText"]>
NAME specifies the name of the button object as a property of the enclosing form object and can be accessed using the name property. VALUE specifies the label to display on the button face and can be accessed using the value property.
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.
％
％ 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.
