Grain Quality Evaluation Method Based on Combination of FNN Neural Networks with D-S Evidence Theory

Author

Zhu, Yuhua ; Zhen, Tong

Author_Institution

Coll. of Inf. Sci. & Eng., Henan Univ. of Technol., Zhengzhou, China

fYear

2009

fDate

11-13 Dec. 2009

Firstpage

1

Lastpage

4

Abstract

The output of the fuzzy neural network was adopted as BPAF (basic probability assignment function) in this paper. By training the fuzzy neural network, the massive language fuzzy information and the concerned expert´s experience were integrated in the decision process, it advantageous in enhancing the BPAF of the accuracy, the reliability and the objectivity. Therefore, using the superiority of D-S evidence theory in the processing uncertainty aspect and analyzing the situation of grain by FNN and the D-S evidence theory union, it can greatly decrease the uncertainty of system.

Keywords

agricultural products; fuzzy neural nets; inference mechanisms; probability; D-S evidence theory; FNN neural network; basic probability assignment function; fuzzy neural network; grain quality evaluation; Decision making; Educational institutions; Electronic mail; Fuzzy neural networks; Humidity; Information science; Neural networks; Power system modeling; Temperature sensors; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-4507-3

Electronic_ISBN

978-1-4244-4507-3

Type

conf

DOI

10.1109/CISE.2009.5362739

Filename

5362739