EconPapers    
Economics at your fingertips  
 

Neural and Evolutionary Computation Methods for Spatial Classification and Knowledge Acquisition

Yee Leung
Additional contact information
Yee Leung: The Chinese University of Hongkong

Chapter 4 in GeoComputational Modelling, 2001, pp 71-108 from Springer

Abstract: Abstract Non-linearity, complexity and dynamics have become a focal point of research in spatial analysis, especially in the analysis of spatial data. Regardless of what we are dealing with, the need to handle systems with a high degree of complexity is now the rule rather than the exception. With our spatial systems becoming more and more complex and highly fluid, non-linearity prevails and evolution is full of surprises. It is essential to develop approaches which can effectively analyse complexity, non-linearity and dynamics in spatial systems in general, and in particular spatial classification and knowledge acquisition.

Keywords: Genetic Algorithm; Fuzzy Number; Fuzzy System; Fuzzy Rule; Radial Basis Function Neural Network (search for similar items in EconPapers)
Date: 2001
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:adspcp:978-3-662-04637-1_4

Ordering information: This item can be ordered from
http://www.springer.com/9783662046371

DOI: 10.1007/978-3-662-04637-1_4

Access Statistics for this chapter

More chapters in Advances in Spatial Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-04-01
Handle: RePEc:spr:adspcp:978-3-662-04637-1_4