By Pierre D'hondt, Etienne E Kerre, Da Ruan
FLINS, initially an acronym for "Fuzzy common sense and clever applied sciences in Nuclear Science", has now been prolonged to incorporate computational clever structures for utilized examine. FLINS 2004, is the 6th in a sequence of foreign meetings, covers state of the art examine and improvement in utilized computational intelligence for utilized examine normally and for power/nuclear engineering specifically. This booklet provides the most recent learn developments and destiny examine instructions within the box.
Read or Download Applied Computational Intelligence: Proceedings of the 6th International FlINS Conference, Blankenberge, Belgium, September 1-3, 2004 PDF
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Additional info for Applied Computational Intelligence: Proceedings of the 6th International FlINS Conference, Blankenberge, Belgium, September 1-3, 2004
35, 13-24. 8. , Xu, Y. (1999), Fuzzy LI-ideals in LIAs, Journal of Fuzzy Mathematics, 7 (4), 997- 1003. 9. H. (2000), Intuitionistic fuzzy LI-ideals in LIAs, Sci. , 3 (2), 185-192. 10. , Ma, J. (2002), &Reasoning principle based on lattice-valued modal propositional logic LMP(X), Proc. ,Cyber. ,July 14-18, Orlando, USA, Vol. XVI, 114-118. 11. Y. (2003), ILI-ideals and Prime LI -ideals in LIAs, Information Sciences, 155, 157-175. 12. M. (2003), A resolution-like strategy based on a lattice-valued logic, IEEE Transaction on Fuzzy Systems, 11 (4), 560-567.
6 and 9 for more details) that these neural units have high efficiency for signal processing and control problems. 15 X Figure 1. The higher-order neural unit 3. Edge detection using higher-order neural units and applications to mobile robot routing problems Traditional edge detection and enhancement techniques include Sobel, Laplace and Gabor operators, etc. 8>10Most of them are based on the multi-layer feedforward network architectures, which usually have longer learning time. In our present research, we developed a new neural unit by which using one unit is capable of dealing with complicated problems.
An edge is where the vertical and the horizontal surfaces of objects meet. For a mobile robot, an edge may mean where the path ends and the wall or obstacle starts, or where the paths and obstacles intersect. Traditionally, edges have been defined loosely as pixel intensity discontinuities within an image. Usually it is easy to detect edges with high contrast, or those with high S / N ratio. But in changing light or dim circumstances, the edge 13 14 detection may not be so easy. In the mean time, the on-board CPU should recognize the feasible paths and path junctions or crossings using the edge features.
Applied Computational Intelligence: Proceedings of the 6th International FlINS Conference, Blankenberge, Belgium, September 1-3, 2004 by Pierre D'hondt, Etienne E Kerre, Da Ruan
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