@INPROCEEDINGS{id:Siminski2008neuropartition,
  author = {Krzysztof Simi\'{n}ski},
  title = {Neuro-fuzzy system with hierarchical domain partition},
  booktitle = {Proceedings of the International Conference on Computational Intelligence
	for Modelling, Control and Automation (CIMCA 2008)},
  year = {2008},
  pages = {392--397},
  address = {Vienna, Austria},
  publisher = {{IEEE Computer Society Publishing}},
  abstract = {The paper presents the hierarchical domain partition in the neuro-fuzzy
	system with parameterized consequences. The hierarchical domain partition
	has the advantages of grid partition and clustering. It avoids the
	curse of dimensionality and the problem of determination of number
	of regions. This method of domain partition reduces the occurrence
	of areas with low membership to all regions. The paper depicts the
	iterative procedure of hierarchical split based on finding and splitting
	the region with the highest contribution to the error of the system.
	The split of regions into two subregions in the proposed system is
	based on the fuzzy clustering, resulting in both splitting and fuzzyfication
	of the subregions. Both decisive and error values are taken into
	consideration in splitting the regions. The paper presents the results
	of experiments on real life and synthetic datasets. This approach
	can produce neuro-fuzzy inference systems with better generalisation
	ability and subsequently lower error rate.},
  doi = {http://dx.doi.org/10.1109/CIMCA.2008.67},
  isbn = {978-0-7695-3514-2},
  timestamp = {2009.02.26},
     note = {[8 pkt; Web of Science]},

}