A fast method for fuzzy neural modeling and refinement

In the identification of complex dynamic systems using fuzzy neural networks, one of the main issues is the curse of dimensionality, which makes it difficult to retain a large number of system inputs or to consider a large number of fuzzy sets. Moreover, due to the correlations, not all possible network inputs or regression vectors in the network are necessary and adding them simply increases the model complexity and deteriorates the network generalisation performance. In this paper, the problem is solved by first proposing a fast algorithm for selection of network terms, and then introducing a refinement procedure to tackle the correlation issue. Simulation results show the efficacy of the method.

Characterization of polymorphic microsatellites for the rough periwinkle gastropod, Littorina saxatilis (Olivi, 1792) and their cross-amplification in four congeners.

Eight new microsatellite loci were characterized for Littorina saxatilis (Olivi, 1792) and tested for their cross-hybridization in congeners. All loci were polymorphic in Irish and Celtic Sea samples, with an average number of alleles per locus of 15 (range, 6–31). Observed and expected locus heterozygosities ranged from 26 to 85% and from 53 to 92%, respectively. Three loci showed excess homozygosity and significant departures from Hardy–Weinberg expectations in one sample, possibly due to null alleles, population structuring or inbreeding. No linkage disequilibrium was detected among loci within samples. A high degree of cross-hybridization was observed in closely related congeners and most loci were polymorphic. These markers will be useful for investigating population genetic diversity and connectivity in coastal populations, especially for marine reserve design.