TrainReset:there are many trains need to drive out of the station .In terms of their numbers from small to large.There are only three railways used to place the trains ,and also need the same order.
标签: TrainReset numbers station trains
上传时间: 2015-07-03
上传用户:moerwang
KMEANS trains a k means cluster model.CENTRES = KMEANS(CENTRES, DATA, OPTIONS) uses the batch K-means algorithm to set the centres of a cluster model. The matrix DATA represents the data which is being clustered, with each row corresponding to a vector. The sum of squares error function is used. The point at which a local minimum is achieved is returned as CENTRES.
标签: CENTRES KMEANS OPTIONS cluster
上传时间: 2014-01-07
上传用户:zhouli
Silmulation of connecting trains
标签: Silmulation connecting trains of
上传时间: 2013-12-20
上传用户:bruce5996
The neuro-fuzzy software for identification and data analysis has been implemented in the MATLAB language ver. 4.2. The software trains a fuzzy architecture, inspired to Takagi-Sugeno approach, on the basis of a training set of N (single) output-(multi) input samples. The returned model has the form 1) if input1 is A11 and input 2 is A12 then output =f1(input1,input2) 2) if input1 is A21 and input 2 is A22 then output =f2(input1,input2) 看不懂,据高手说,非常有用。
标签: identification neuro-fuzzy implemented analysis
上传时间: 2014-01-12
上传用户:zgu489
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. %
标签: back-propagation corresponding input-output algorithm
上传时间: 2016-12-26
上传用户: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.
标签: Levenberg-Marquardt desired network neural
上传时间: 2016-12-26
上传用户:jcljkh
Wireless Fidelity (Wi-Fi) networks have become mainstream over the last few years. What started out as cable replacement for static desktops in indoor networks has been extended to fully mobile broadband applications involving moving vehicles, high-speed trains, and even airplanes.
标签: Technologies Emerging Wireless Theory LANs in
上传时间: 2020-05-27
上传用户:shancjb