ICS Dr. G. Roscher GmbH
Artificial Neuronal Network
Nonlinear Feature Extraction
computers to model neurones and their interconnections presents an interesting
way to simulate the way information processes work. With the development of the
neuronal network today's highest-performance tools are offered which are user
specific and easy to learn. Although one must treat with some scepticism the
claim that this technology functions in a manner similar to that of human
thought, nevertheless it brings with it many useful new options.
Artificial neuronal networks provide the model for intelligent systems and in many cases allow pattern recognition. They are characterize because of their high tolerance for error and their ability to self-organise. Their implementation on powerful computers running in parallel greatly increases the number of applications which can be successfully addressed. Artificial neuronal networks provide totally new options for solutions to problems which until now were insoluble.
Powerful development tools for the design and installation of neuronal
Installation under graphic user interface.
Flexible network input and output options.
"Hidden layers" option.
Generation of initial values.
Fast and effective training.
Graphic representation of training progress.
Support in investigating the efficacy of the neuronal network.