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Proceedings Paper

Spatial dependencies mining based on fuzzy neural networks
Author(s): L. M. Jiao; Y. L. Liu
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Paper Abstract

Spatial dependency describes the relationship between one dependent spatial variable and other related spatial variables. This paper constructs two kinds of Fuzzy Neural Networks for spatial dependency mining, the modified fuzzy neural network model and the fuzzy comprehensive assessment network model. The first model is built from general fuzzy neural network model. It has four layers, input layer, fuzzy membership function layer, fuzzy reasoning layer and output layer. The second model is built based on a fuzzy comprehensive assessment algorithm. It has five layers. The first three layers are same as the first model, the fourth and the fifth layer are used to find the maximum membership degree and give the output. We develop the training algorithm for these two models based BP algorithm and genetic algorithm, respectively. This paper adopts a thematic spatial database of land evaluation to test these models. We use experiential knowledge as original rules to build initial FNN models. We can see that original rules (spatial dependencies) are corrected after training. It can be seen that these two models get almost the same revised dependencies, and this indicates that these two models both correct the original ones and get the more objective spatial dependencies. Experiments also indicate these two models are efficient.

Paper Details

Date Published: 29 December 2008
PDF: 10 pages
Proc. SPIE 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA), 728540 (29 December 2008); doi: 10.1117/12.815957
Show Author Affiliations
L. M. Jiao, Wuhan Univ. (China)
Y. L. Liu, Wuhan Univ. (China)

Published in SPIE Proceedings Vol. 7285:
International Conference on Earth Observation Data Processing and Analysis (ICEODPA)
Deren Li; Jianya Gong; Huayi Wu, Editor(s)

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