A novel graph computation technique for multi-dimensional curve fitting
Curve-fitting problems are widely solved using numerical and soft techniques. In particular, artificial neural networks (ANN) are used to approximate arbitrary input–output relationships in the form of tuned edge weights. Moreover, using semantic networks such as fuzzy cognitive map (FCM), single gr...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/11022/1/09540091.2013.pdf http://eprints.utem.edu.my/id/eprint/11022/ http://www.tandfonline.com/doi/abs/10.1080/09540091.2013.851173#.UvGgWdHNuUk |
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| Summary: | Curve-fitting problems are widely solved using numerical and soft techniques. In particular, artificial neural networks (ANN) are used to approximate arbitrary input–output relationships in the form of tuned edge weights. Moreover, using semantic networks such as fuzzy cognitive map (FCM), single graph nodes could be directly associated with their actual grey scales rather than binary values as in ANN. This article examines a novel methodology for automatic construction of FCMs for function approximation. The main contribution is the introduction of nested-FCM structure for multi-variable curve fitting. There are step-by-step example cases along with the obtained results to serve as a guide to the new methods being introduced. It is shown that nested FCM derives relationship models of multiple variables using any conventional weight training technique with minimal computation effort. Issues about computational cost and accuracy are also discussed along with future direction of the research. |
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