* Lightweight backpropagation neural network.
* This a lightweight library implementating a neural network for use
* in C and C++ programs. It is intended for use in applications that
* just happen to need a simply neural network and do not want to use
* needlessly complex neural network libraries. It features multilayer
* feedforward perceptron neural networks, sigmoidal activation function
* with bias, backpropagation training with settable learning rate and
* momentum, and backpropagation training in batches.
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.
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.
OReilly.Java.Rmithis book provides strategies for working with serialization,
threading, the RMI registry, sockets and socket factories, activation,
dynamic class downloading, HTTP tunneling, distributed garbage
collection, JNDI, and CORBA. In short, a treasure trove of valuable
RMI knowledge packed into one book.
NN Functions
a program in Lisp to demonstrate working of an artificial neuron. (Enter an input vector X and weight vector W. Calculate weighted sum XW. Transform this using signal or activation functions like logistic, threshold, hyperbolic-tangent, linear, exponential, sigmoid or some other functions (syntax provided) and display the output).
ADIAL Basis Function (RBF) networks were introduced
into the neural network literature by Broomhead and
Lowe [1], which are motivated by observation on the local
response in biologic neurons. Due to their better
approximation capabilities, simpler network structures and
faster learning algorithms, RBF networks have been widely applied in many science and engineering fields. RBF network is three layers feedback network, where each hidden unit implements a radial activation function and each output unit implements a weighted sum of hidden units’ outputs.
最新正版solidworks2017安裝教程目前，solidworks最新版本是solidworks2017，功能齊全，操作簡便。下面，為大家介紹一下solidworks2017安裝教程。安裝步驟：1.斷開電腦網絡，鼠標右擊SolidWorks.2017.Activator-SSQ，進行解壓2.打開解壓之后的文件夾，鼠標右擊SW.Activator，選擇以管理員的身份運行3.首先點擊左側的 set serial numbers然后右側選擇 force local activation serial numbers，最后點擊 accept serial numbers4.點擊Yes后，繼續(xù)點擊OK5.再點擊左邊的“Activate Licenses”，確認Status項中的值都是“Activate”，然后點擊“Activate Licenses”，彈出窗口點擊“NO
