Winkler, Jonas; Hauke, Sascha; Pyka, Martin; Heider, Dominik:
JACVANN: A Java Framework For Complex Valued Artificial Neural Networks
2010
In: Systemics and Informatics World Network, Band 10 (2010), S. 48 - 55
Artikel/Aufsatz in Zeitschrift2010InformatikBiologie
Titel:
JACVANN: A Java Framework For Complex Valued Artificial Neural Networks
Autor(in):
Winkler, JonasLSF; Hauke, Sascha; Pyka, Martin; Heider, DominikLSF
Erscheinungsjahr
2010
Erschienen in:
Titel:
Systemics and Informatics World Network
in:
Band 10 (2010), S. 48 - 55
ISSN:

Abstract:

JACVANN (Java Framework for Complex Valued Artificial Neural Networks) is an open source framework for complex-valued artificial neural networks. It provides several initialization procedures, such as the Nguyen-Widrow method, learning algorithms, such as backpropagation and resilient propagation, as well as different evaluation methods for complex-valued classification problems. Furthermore, we implemented a procedure for transferring real-valued problems into complex space to harness the strengths of our framework. The framework was tested with several geometric and classification problems from the literature. It is shown that JACVANN is both robust and flexible due to its modular composition. Availability: http://www.uni-due.de/~hy0546/JACVANN/ Contact: dominik.heider@uni-due.de Requirements: Java 5.0 or higher