Evenet 2000: Designing and Training Arbitrary Neural Networks in Java

In this paper, Evenet-2000, a Java-Based neural network toolkit is presented. It is based on the representation of an arbitrary neural network as a block diagram (these blocks are, for example, summing junctions or branch points) with a set of simple manipulation rules. With this toolkit, users can...

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Hauptverfasser: González, Evelio J., Hamilton, Alberto F., Moreno, Lorenzo, Sigut, José F., Marichal, Roberto L.
Format: Buchkapitel
Sprache:eng
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Zusammenfassung:In this paper, Evenet-2000, a Java-Based neural network toolkit is presented. It is based on the representation of an arbitrary neural network as a block diagram (these blocks are, for example, summing junctions or branch points) with a set of simple manipulation rules. With this toolkit, users can easily design and train any arbitrary neural network, even time-dependent ones, avoiding the complicated calculations that the means of establishing the gradient algorithm requires when a new network architecture is designed. Evenet-2000 consists of three parts: a calculation library, a user-friendly interface and a graphic network editor with all the Java advantages: encapsulation, inheritance, powerful libraries...
ISSN:0302-9743
1611-3349
DOI:10.1007/3-540-45723-2_12