Spatial dependencies mining based on fuzzy neural networks

L. M. Jiao; Y. L. Liu

doi:10.1117/12.815957

29 December 2008 Spatial dependencies mining based on fuzzy neural networks

L. M. Jiao, Y. L. Liu

Proceedings Volume 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA); 728540 (2008) https://doi.org/10.1117/12.815957
Event: International Conference on Earth Observation Data Processing and Analysis, 2008, Wuhan, China

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.

Citation Download Citation

L. M. Jiao and Y. L. Liu "Spatial dependencies mining based on fuzzy neural networks", Proc. SPIE 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA), 728540 (29 December 2008); https://doi.org/10.1117/12.815957

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available