Abstract
The need for more effective methods to generate and maintain global nonfunctional properties suggests an approach analogous to those of natural processes in generating emergent properties. Emergent model allows the constraints of the task to be represented more naturally and permits only pertinent task specific knowledge to emerge in the course of solving the problem. The paper describes some basics of emergent phenomena and its implementation in the rough hybrid systems.
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Hassan, Y., Tazaki, E. (2003). Interpretation of Rough Neural Networks as Emergent Model. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2003. Lecture Notes in Computer Science(), vol 2639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39205-X_31
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DOI: https://doi.org/10.1007/3-540-39205-X_31
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