<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>To identify the important attributes of complex system, which is high-dimensional and contain both discrete and continuous variables, this paper proposes a sensitivity analysis method of hierarchical hybrid fuzzy - neural network. We derive the sensitivity indexes of discrete and continuous variables through the differential method. To verify the effectiveness of our method, this study employed a man-made example and a remote sensing image classification example to test the performance of our method. The results show that our method can really identify the important variables of complex system and discover the relations between input and output variables; therefore, they can be applied to simplify the model and improve the classification accuracy of model.</p></abstract>

To identify the important attributes of complex system, which is high-dimensional and contain both discrete and continuous variables, this paper proposes a sensitivity analysis method of hierarchical hybrid fuzzy - neural network. We derive the sensitivity indexes of discrete and continuous variables through the differential method. To verify the effectiveness of our method, this study employed a man-made example and a remote sensing image classification example to test the performance of our method. The results show that our method can really identify the important variables of complex system and discover the relations between input and output variables; therefore, they can be applied to simplify the model and improve the classification accuracy of model.

Sensitivity Analysis of Hierarchical Hybrid Fuzzy - Neural Network

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Sensitivity Analysis of Hierarchical Hybrid Fuzzy - Neural Network"}

To identify the important attributes of complex system, which is high-dimensional and contain both discrete and continuous variables, this paper proposes a...

Sensitivity Analysis of Hierarchical Hybrid Fuzzy - Neural Network

Pubblicato online: 01 set 2015

Pagine: 1837 - 1854

Ricevuto: 15 apr 2015

Accettato: 12 lug 2015

DOI: https://doi.org/10.21307/ijssis-2017-832

© 2015 Xing Haihua et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.