Fuzzy Systems Based on Multispecies PSO Method in Spatial Analysis | Zendy

Ferdinando Di Martino | Zendy; Vincenzo Loia | Zendy; Salvatore Sessa | Zendy

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Fuzzy Systems Based on Multispecies PSO Method in Spatial Analysis

Author(s) -

Ferdinando Di Martino,

Vincenzo Loia,

Salvatore Sessa

Publication year - 2012

Publication title -

advances in fuzzy systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.38

H-Index - 19

eISSN - 1687-711X

pISSN - 1687-7101

DOI - 10.1155/2012/808361

Subject(s) - fuzzy logic , particle swarm optimization , fuzzy set , computer science , set (abstract data type) , cluster (spacecraft) , data mining , spatial analysis , similarity (geometry) , mathematics , artificial intelligence , statistics , algorithm , image (mathematics) , programming language

We present a method by using the hierarchical cluster-based Multispecies particle swarm optimization to generate a fuzzy system of Takagi-Sugeno-Kang type encapsulated in a geographical information system considered as environmental decision support for spatial analysis. We consider a spatial area partitioned in subzones: the data measured in each subzone are used to extract a fuzzy rule set of above mentioned type. We adopt a similarity index (greater than a specific threshold) for comparing fuzzy systems generated for adjacent subzones.

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