Trajan
survey performed by Juha Ikonen, May 21st 1999
Trajan 4.0 is a fully-featured Neural Network simulation
package. It includes support for a wide range of Neural Network types,
training algorithms, and graphical and statistical feedback on Neural
Network performance.
The SOM (or Kohonen Network) is only one of the supported network
types and the approach is somewhat different from other applications
implementing the SOM. The Kohonen Network is treated as one special
case of neural networks in general and some of the special features of
SOM have not been implemented. Major defincies include
- map topology and shape are always square
- some of the useful visualization tools are not included such as U-matrix and trajectory
of BMUs
On the other hand many features have been implemented very well,
these include preprocessing tools and labelling. Also the speed of
training is worth mentioning. In general Trajan is a good package for
various purposes and if one does not need advanced visualization
tools, Trajan is well suitable also for SOM applications.
Disclaimer: If any information on this page simply is not
true, please tell us about it and we'll correct it ASAP.
Disclaimer: The opinions and observation herein should be
considered personal of the person having performed by the survey, at
the time of the survey. They do not reflect any official standing of
his employer, of the Laboratory of Computer and Information Science or
the Neural Networks Research Center.
General
Program name |
Trajan Neural Networks Simulator, Release 4.0 A |
Availability |
Commercial software, demonstration version is available at http://www.trajan-software.demon.co.uk.
Pricing:
Standard
Academic
USA, Canada and Mexico: 795
595 (US
dollars)
Other countries: 595
450 (UK
pounds sterling)Company information:
Trajan Software Ltd.
Trajan House,
68 Lesbury Close,
Chester-le-Street,
Co. Durham, DH2 3SR
United Kingdom
tel/fax: +44 (191) 388 5737
email: andrew@trajan-software.demon.co.uk. |
Purpose |
Fully-featured Neural Network simulation package for variety of purposes. |
Operating system |
32-bit Windows (Windows 95/98, Windows NT) |
User interface |
Easy to use graphical user interface. |
Documentation |
Extensive online help and a 350-page user manual. |
Other |
Trajan's features can be used also programmatically via an Application Programming
Interface (API), application samples are included. |
SOM features
map parameters |
Teaching algorithm |
Standard SOM algorithm,
implementation seems ok. |
Map size |
2-dimensional map lattice.
Smallest 1x1, maximum size is limited by the amount of available memory. |
Map lattice and shape |
Both rectangular |
Neighborhood function |
Function type: square |
Neighborhood size (h): decreases linearly from the start to end values during
the training. |
Learning rate (alpha): is altered linearly from the first to last epochs. |
Initialization |
By default the map weights are treated as vectors and set to unit length. Other
available methods are
- uniform within a range of minimum and maximum values,
- Gaussian with mean and standard deviation and
- zero, the weights are all set to zero. |
Distance function |
Probably euclidian, not verified. |
Unknown components |
Allowed |
Teaching length |
In epochs, stopping conditions may also be specified. Additionally, if over-learning
occurs, the best network discovered during the training can be retrieved. |
efficiency |
Speed
[Windows NT 4.0, 333 MHz Pentium II, 128 MB RAM] |
for ~3000 13-dim data samples, 10 epochs
training time: 7 seconds |
Results |
seemed ok
quantization error: 0.3358 |
[Comments on SOM implementation]
Usability
preprocessing |
Input formats |
Variety of standard ascii formats (tab/comma/space separated), STATISTICA and Trajan's
own Fast binary data file format. |
Data handling and selection |
Very good tools for data preprocessing:
- a special data set editor, where user can edit data and select which components and data
vectors to use,
- data can be scaled using several methods,
- missing values can be substituted by mean, median, minimum, maximum or zero value,
- nominal (non numeric) variables can be substituted by numerical values,
- useless features (vector components) can be ignored from input data set automatically. |
postprocessing |
Output formats |
Variety of standard ascii formats (tab/comma/space separated), STATISTICA and Trajan's
own Fast binary data file format. |
Map measures |
Quantization error (RMS) |
Labeling |
Advanced: labels can be set by user or automatically |
Clustering |
By visualization |
visualization |
Inspection of neurons |
Simple: weight vectors can be inspected, no graphical representation available. |
Clusters/map shape |
Topological map |
Correlations |
By inspecting weight vectors |
Data projections |
BMU's can be found for a single vector or for entire test set. |
Markers |
Labels |
![](/style/basic/gpixel.gif)
http://www.cis.hut.fi/projects/somtoolbox/links/trajan.shtml
somtlbx@mail.cis.hut.fi
Monday, 09-Oct-2000 12:53:09 EEST
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