ANN1.0
所属分类:人工智能/神经网络/深度学习
开发工具:Others
文件大小:41KB
下载次数:5
上传日期:2006-11-30 12:49:19
上 传 者:
mamatelitursun
说明: ANN 1.0 人工神经网络程序, Artificial Neural Network Training Program ANN.EXE v1.10,
(artificial neural network program, Artificial Neural Network Training Program AN N. EXE v1.10.)
文件列表:
BP (0, 1997-01-11)
BP\CSTR.CFG (461, 1996-12-01)
BP\CSTR_N1.DAT (1024, 1996-11-08)
BP\CSTR_N2.DAT (1024, 1996-11-08)
BP\W0.DAT (579, 1996-11-23)
BP\WF.DAT (572, 1996-12-01)
BP\CSTR.DTL (19990, 1996-12-01)
BP\ANN.EXE (70038, 1996-11-23)
BP\TEST.ITR (1241, 1996-12-01)
BP\TRAINING.ITR (112, 1996-11-23)
BP\TRNG.ITR (1242, 1996-12-01)
Artificial Neural Network Training Program
ANN.EXE v1.10, Copyright (c) Sommnath Kundu
1994-1996
DESCRIPTION:
-----------
This artificial neural network program (ann.exe) was developed in the
Department of Chemical Engineering, Calcutta University, India, as a
part of a research project on the application of ANN in the field of
Chemical Engineering. It may be quite helpfull to those who are doing
research or interested in this field. It is a general purpose ANN
training program. You can use input/output data for any system to
construct and train the network. To effectively simulate the system
you must include all the key parameters of the system as input or
output of the net. This program is menu driven and will ask you for
required inputs. All the options will be saved in a configurayion file,
so you can use it later as a command line parameter. Followings are
some of the usefull features of the program,
1.) Any size of the network can be constructed as permitted by DOS.
2.) Different training algorithms are available, e.g., steepest
descent, conjugate gradient, DFP, BFGS and Maquardt-Levenberg.
3.) A heuristic search algorithm partially based on quadratic
interpolation, developed by the author, is used to find out
optimum stepsize for each iteration. This algorithm increases
training speed and accuracy many times.
4.) Different mode of training can be selected, e.g., pattern,
moving window and block mode of training.
5.) Activation function can be selected.
6.) Input and output data to the net can be scaled automatically.
So you can instantly compare and verify the trained net for the
test data set.
7.) Various intermediate results during training can be stored in
the data files to facilitate statistical analysis.
There is a single executable file, ann.exe. You can run it interactively
or non-interectively by giving configuration file name as a command-line
parameter. All the data files used by the program are formatted as a text
file, so you can use externally created data file, but the format should
be the same.
SAMPLE DATA:
-----------
A set of sample data files are included for a model of N-number
of equal volume CSTR (constant stirred tank reactor) connected
in a series having concentration CA0 (of A). At time t0 some amount
solution A of concentration CA is fed into 1st tank. The concentration
of A in the last tank is calculated as a function of time and number
of tanks present in the series. The concentrations at different time
interval are used as inputs and number of tanks as outputs to train
the net. The data files included with this program are as folows:
CSTR_N1.DAT ==> Training data set for CSTR model
CSTR_N2.DAT ==> Test data set for CSTR model
W0 ==> Initial weights
W1 ==> Final weights (to be calculated)
TRNG.ITE ==> Iteration record file for training data
TEST.ITR ==> Iteration record file for test data
CSTR.DTL ==> File to save details calculation
CSTR.CFG ==> Net configuration file
You can train the net for this sample data by giving net configuration
file (CSTR.CFG) when asked by ANN.EXE or giving it as a command-line
parameter, e.g., ann cstr.cfg.
All the above data files will be generated for your data file.
COPYRIGHT:
---------
ANN v1.10 can be freely distributed, but it should not be used or
distributed for any commercial purposeS without prior permission
of the author.
In case of any problem or question you can contact the author in the
following addresses.
Somnath kundu
Office: mds@giascl01.vsnl.net.in
Residence: 44/A Madhu Roy Lane,
Calcutta - 700 006, India.
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