Neural Network Approaches to Spatial Knowledge Representation and Inference

Yee Leung
Additional contact information
Yee Leung: The Chinese University of Hong Kong

Chapter 5 in Intelligent Spatial Decision Support Systems, 1997, pp 173-227 from Springer

Abstract: Abstract Our discussion so far has concentrated on the symbolic approaches to spatial knowledge representation and inference. Logic (fuzzy and non-fuzzy), production systems, semantic networks, frames, object-oriented programming, and their hybrids all belong to symbolic systems in which knowledge is modeled by symbols. Intelligence is realized by a symbolic structure in which symbols can be manipulated and reasoning can be made. The advantages of the symbolic approaches are that they provide a structured representation of knowledge so that processing elements corresponding to meaningful concepts and inference can be traced and explained. The separation of knowledge from the inference mechanism also makes knowledge update easier and more efficient. The approach is thus a top down approach which gives consensus knowledge to a system by instructing it what to feel and respond without having to gain knowledge through experience. It may be a faster way to build intelligent system.

Keywords: Associative Memory; Radial Basis Function Neural Network; Feedforward Neural Network; Recurrent Neural Network; Neural Network Approach (search for similar items in EconPapers)
Date: 1997
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:978-3-642-60714-1_5

Ordering information: This item can be ordered from
http://www.springer.com/9783642607141

DOI: 10.1007/978-3-642-60714-1_5

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 ().