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dANN is a [[library (computing)|library]] of implementations of artificial intelligence. It's goal is to create a useful set of methods that can be used in conventional software development or as a base to research and further development of more sophisticated methods.<ref>The dANN project website - http://wiki.syncleus.com/index.php/DANN</ref> |
dANN is a [[library (computing)|library]] of implementations of artificial intelligence. It's goal is to create a useful set of methods that can be used in conventional software development or as a base to research and further development of more sophisticated methods.<ref>The dANN project website - http://wiki.syncleus.com/index.php/DANN</ref> |
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The library extensively uses the approach of using [[neural networks|artificial neural network]] for adaptive logic and data evaluation. As neural network show great adaptability to the data being input they are ideal for analysis of sophisticated data structures. The flexibility of the computation model can be used for versatile applications like decision making. |
The library extensively uses the approach of using [[neural networks|artificial neural network]] for adaptive logic and data evaluation. As neural network show great adaptability to the data being input they are ideal for analysis of sophisticated data structures.<ref>Predictive non-linear modeling of complex data by ANN - http://www.ncbi.nlm.nih.gov/pubmed/11849962</ref> The flexibility of the computation model can be used for versatile applications like decision making. |
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The dANN library separates the functionality into several categories. |
The dANN library separates the functionality into several categories. |
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The graph theory contains various methods of path searching algorithms such as [[A*]], [[Djikstra's algorithm|Djikstra's]], [[Bellman-Ford algorithm]], [[Johnson's algorithm]], [[Floyd-Warshall algorithm]] and the [[Hill climbing|Hill Climbing Local Search algorithm]]. |
The [[graph theory]] contains various methods of path searching algorithms such as [[A*]], [[Djikstra's algorithm|Djikstra's]], [[Bellman-Ford algorithm]], [[Johnson's algorithm]], [[Floyd-Warshall algorithm]] and the [[Hill climbing|Hill Climbing Local Search algorithm]]. |
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Latest revision as of 16:09, 27 December 2009
- WIP
| Original author(s) | ???Jeffrey Phillips Freeman - Syncleus |
|---|---|
| Developer(s) | open development |
| Stable release | 1.x - build #152
/ December 2, 2009 |
| Written in | ???Java, (C++ and C# compatible) |
| Operating system | Platform independent |
| Size | 19MB |
| Type | library |
| License | OSCL Type C |
| Website | The dANN Project website |
dANN is a library of implementations of artificial intelligence. It's goal is to create a useful set of methods that can be used in conventional software development or as a base to research and further development of more sophisticated methods.[1]
The library extensively uses the approach of using artificial neural network for adaptive logic and data evaluation. As neural network show great adaptability to the data being input they are ideal for analysis of sophisticated data structures.[2] The flexibility of the computation model can be used for versatile applications like decision making. The dANN library separates the functionality into several categories. The graph theory contains various methods of path searching algorithms such as A*, Djikstra's, Bellman-Ford algorithm, Johnson's algorithm, Floyd-Warshall algorithm and the Hill Climbing Local Search algorithm.
- ^ The dANN project website - http://wiki.syncleus.com/index.php/DANN
- ^ Predictive non-linear modeling of complex data by ANN - http://www.ncbi.nlm.nih.gov/pubmed/11849962