Share Email Print
cover

Proceedings Paper

An intelligent computational algorithm based on neural network for spatial data mining in adaptability evaluation
Author(s): Zuohua Miao; Hong Xu; Yong Chen; Xiangyang Zeng
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Back-propagation neural network model (BPNN) is an intelligent computational model based on stylebook learning. This model is different from traditional adaptability symbolic logic reasoning method based on knowledge and rules. At the same time, BPNN model has shortcoming such as: slowly convergence speed and partial minimum. During the process of adaptability evaluation, the factors were diverse, complicated and uncertain, so an effectual model should adopt the technique of data mining method and fuzzy logical technology. In this paper, the author ameliorated the backpropagation of BPNN and applied fuzzy logical theory for dynamic inference of fuzzy rules. Authors also give detail description on training and experiment process of the novel model.

Paper Details

Date Published: 10 November 2008
PDF: 11 pages
Proc. SPIE 7146, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses, 71461B (10 November 2008); doi: 10.1117/12.813138
Show Author Affiliations
Zuohua Miao, Wuhan Univ. of Science and Technology (China)
Hong Xu, Wuhan Univ. of Science and Technology (China)
Yong Chen, Wuhan Univ. of Science and Technology (China)
Xiangyang Zeng, Wuhan Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 7146:
Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses
Lin Liu; Xia Li; Kai Liu; Xinchang Zhang, Editor(s)

© SPIE. Terms of Use
Back to Top