文件列表:

40000 Samples For 4Ch_4 - Network.fig (3106, 2011-03-08)
40000 Samples For 4Ch_4 - Reward.fig (265217, 2011-03-08)
Binary.m (402, 2011-03-08)
contents.m (4654, 2011-03-08)
create_fit_net.asv (2548, 2011-03-21)
create_fit_net.m (2549, 2011-03-21)
create_fit_net_variable_noise.m (2512, 2011-03-10)
CreateExtendedTrainingSet.m (659, 2011-03-10)
CreateNetworkSpectum.m (1317, 2011-03-10)
CreateNetworkSpectum_Channel_0_1.m (1263, 2011-03-10)
CreateNetworkSpectum_Channel_0_1_Ch2withNoise.m (1323, 2011-03-10)
DataFileFor_rnn_gen__con1_4Ch_4.mat (250239, 2011-03-08)
DataFileFor_rnn_gen_con1_10Ch_10.mat (519889, 2011-03-10)
DataFileFor_rnn_gen_con1_10Ch_10_wts.mat (9190, 2011-03-10)
DataFileFor_rnn_gen_con1_10Ch_10_wts_GOodFor_10000.mat (9190, 2011-03-15)
DataFileFor_rnn_gen_con1_10Ch_10_wts_GoodResults.mat (190537, 2011-03-08)
DataFileFor_rnn_gen_con1_10Ch_40.mat (548927, 2011-03-10)
DataFileFor_rnn_gen_con1_10Ch_40_wts.mat (22510, 2011-03-10)
DataFileFor_rnn_gen_con1_10Ch_40_wts_GoodResults.mat (22510, 2011-03-10)
DataFileFor_rnn_gen_con1_10Ch_40_wts_N.mat (37255, 2011-03-15)
DataFileFor_rnn_gen_con1_10Ch_40_wts_N_Ch2.mat (37752, 2011-03-15)
DataFileFor_rnn_gen_con1_2x4Ch_24 .mat (234468, 2011-03-10)
DataFileFor_rnn_gen_con1_4Ch_16.mat (272166, 2011-03-10)
DataFileFor_rnn_gen_con1_4Ch_16_wts.mat (7337, 2011-03-10)
DataFileFor_rnn_gen_con1_4Ch_16_wts_N.mat (10583, 2011-03-10)
DataFileFor_rnn_gen_con1_4Ch_16_wts_N_Ch2.mat (13391, 2011-03-15)
DataFileFor_rnn_gen_con1_4Ch_2.mat (266672, 2011-03-10)
DataFileFor_rnn_gen_con1_4Ch_2_N.mat (274824, 2011-03-10)
DataFileFor_rnn_gen_con1_4Ch_2_wts.mat (5694, 2011-03-11)
DataFileFor_rnn_gen_con1_4Ch_2_wts_N.mat (8226, 2011-03-10)
DataFileFor_rnn_gen_con1_4Ch_24.mat (234468, 2011-03-10)
DataFileFor_rnn_gen_con1_4Ch_24_wts GoodResults.mat (9185, 2011-03-10)
DataFileFor_rnn_gen_con1_4Ch_24_wts.mat (9185, 2011-03-10)
DataFileFor_rnn_gen_con1_4Ch_4.mat (202295, 2011-03-10)
DataFileFor_rnn_gen_con1_4Ch_4_wts.mat (29924, 2011-03-08)
DataFileFor_rnn_gen_con1_4Ch_4_wts_2to1.mat (7333, 2011-03-15)
DataFileFor_rnn_gen_con1_4Ch_4_wts_GoodResults.mat (29924, 2011-03-08)
DataFileFor_rnn_gen_con1_4Ch_4_wts_N.mat (7569, 2011-03-10)
DataFileFor_rnn_gen_con1_4Ch_4_wts_N_Ch2.mat (10437, 2011-03-15)
DecideNextChannel.m (1467, 2011-03-10)
... ...

RANDOM NEURAL NETWORK SIMULATOR Version 2.0 Date : September, 1, 1999 Author : Hossam Eldin Mostafa Abdelbaki Address : University of Central Florida, : School of Computer Science Email : ahossam@cs.ucf.edu Home Page : http://www.cs.ucf.edu/~ahossam/ Help : read the manual file (rnnsimv2.pdf) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% RNNSIM v.2 package contains a number of m-files for training and evaluation of the random neural network. All functions have been thoroughly tested. After downloading the separate files or the zipped file, make sure that they are stored or extracted in the directory /rnnsimv2 Below an overview of the files contained in this directory along with a brief description of what they do. The on-line help facility explains how to call the different functions. You simply write help in the MATLAB command window. Along with the m-files in this directory you will find a manual for the simulator in PDF format (rnnsimv2.pdf). Start by printing this out and read the release notes. Two simple demonstration programs are given to illustrate how most of the functions work. Good luck Hossam Contents of the directory /rnnsimv2 Main Files (problem independent) ======================================================================================= 1 - train_rnn_gen.m function m file trains the RNN. 2 - test_rnn_gen.m function m file tests the RNN. 3 - rnn_gen_test_exact.m function m file tests the RNN in batch mode by solving the nonlinear equations of the model. 4 - rnn_gen_test_iterative.m function m file tests the RNN in batch mode without solving the nonlinear equations of the model. ======================================================================================= General Files (problem independent) ======================================================================================= 5- vardef.m script m file declares the global variables. 6- itialize.m script m file initializes the RNN. 7- read_connection_matrix.m function m file reads the connection file. 8- load_net_file.m function m file loads the network file. 9- load_trn_file.m script m file loads the training data file. 10- load_tst_file.m function m file loads the testing data file. 11- initialize_weights.m function m file initializes the network weights. 12- etract_name.m function m file extracts a file name without its extension. ======================================================================================= Documentation ======================================================================================= 13- rnnsimrep.pdf PDF file technical report that describes how to use the RNNSIM v.2. 14- readme.txt ASCII file readme file. 15- contents.m ======================================================================================= Examples Directories ======================================================================================= 24- example1 directory contains files of example 1. 25- example2 directory contains files of example 2. ======================================================================================= Contents of the directory /rnnsimv2/example1 ======================================================================================= use_rnn_gen1.m script m file main file for example 1. rnn_gen_net1.m script m file defines the learning parameters. rnn_gen_con1.dat ASCII data file contains the network connections rnn_gen_wts1.mat Mat file contains the weights. rnn_gen_trn1.m script m file contains the training data. rnn_gen_tst1.m script m file contains the testing data. rnn_gen_log1.txt ASCII data file results of testing the trained network. rnn_gen_log1.m script m file results of testing the trained network. ======================================================================================= Contents of the directory /rnnsimv2/example2 ======================================================================================= use_rnn_gen2.m script m file main file for example 2. rnn_gen_net2.m script m file defines the learning parameters. rnn_gen_con2.dat ASCII data file contains the network connections rnn_gen_wts2.mat Mat file contains the weights. rnn_gen_trn2.m script m file contains the training data. rnn_gen_tst2.m script m file contains the testing data. rnn_gen_log2.txt ASCII data file results of testing the trained network. rnn_gen_log2.m script m file results of testing the trained network. ======================================================================================= Basically, any problem should have a main file, connection file, and net file. The parameters of the network will be saved to the weight file. (All other files are optional).

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