The Application in Grain Monitoring Based on Particle Swarm-Fuzzy Neural Network Algorithm

Zhen Tong; Wang Jing; Yi Ming; Jian Jun Wu

doi:10.4028/www.scientific.net/KEM.474-476.1116

Paper Titles

Use of Municipal Solid Waste Incineration Bottom Ash in Adsorption of Heavy Metals
p.1099

Application of Wavelet Packet Energy Spectrum in Coal-Rock Interface Recognition
p.1103

Simulation Study on Attrition to Centrifugal Sewerage Pump
p.1107

Application in Die Forging Simulation Coupling Finite Element and Element-Free Galerkin Method
p.1111

The Application in Grain Monitoring Based on Particle Swarm-Fuzzy Neural Network Algorithm
p.1116

Study on LPG Air Fuel Ratio Based on Improved Subtractive Clustering RBF Neural Networks
p.1122

Effect of Oxide Film Thickness on Stress-Strain Field at the Tip of Stress Corrosion Cracking
p.1128

How Quality Influence User’s Continuance of the Recommendation Blog
p.1132

Study on Optimum Structure and Residual Stress of Ti/TiAl Micro-Laminated Sheet
p.1137

HomeKey Engineering MaterialsKey Engineering Materials Vols. 474-476The Application in Grain Monitoring Based on...

The Application in Grain Monitoring Based on Particle Swarm-Fuzzy Neural Network Algorithm

Abstract:

In order to strengthen the administration of the barn, we need to storage environment for scientific and effective monitoring and analysis. This paper firstly filter and merge the temperature and humidity information in the environment by using fuzzy neural network, then regard the temperature and humidity as particles, calculate the fitness of the particles according to corresponding mildew rate and cost, finally the outputs of temperature and humidity curve values are obtained through iterative optimization of local and global extreme by particle swarm algorithm.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Key Engineering Materials (Volumes 474-476)

Pages:

1116-1121

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.474-476.1116

Citation:

Cite this paper

Online since:

April 2011

Authors:

Zhen Tong, Wang Jing, Yi Ming, Jian Jun Wu

Keywords:

BP Neural Network (BPNN), Grain Situation Control System, Grain Situation Measurement System, Particle Swarm

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] J Kennedy J, Eberhart R C. Particle swarm optimization[C]. / Perth, Proc. IEEE Int. Conf. Neural Networks. Australia, 1995: 1942-(1984).

Google Scholar

[2] Mc Cleland J L, Rumelhart D E Explorations in parallel distributed processing, a handbook of model, programs and exercises [M]. Cambridge, MIT Press, (1986).

Google Scholar

[3] Martin T H, Howard B D, Mark H B. Neural network design [M]. PWS Publishing Company, (1996).

Google Scholar

[4] He Yong, Zhu Ke jun et al. An Integrated Model of PSO And ANN for Optimizing Cut-off Grade And Grade of Crude Ore [J]. Operations Research and Management Science, 2009, 18(2): 89- 96.

DOI: 10.1109/icnc.2008.684

Google Scholar

[5] Ma Li, Zhang De-feng. Algorithm research based on generalized dynamic fuzzy neural network [J]. Computer Engineering and Design, 2009, 30(40): 4727- 4730.

Google Scholar

[6] Li Jianyang, Zheng Hanyuan, Liu Huiting. Case-based Reasonor Based on Multi-layered Feedforward Neural Nerwork [J]. Computer Engineering, 2006, 32(7): 188- 190.

Google Scholar

[7] Wu Jianjun, Zhang Qian, Zhou Dongyao. Application of Improved Neural Network Algorithm Based on Particle Swarm in Grain [J]. Journal of Henan of Technology (Natural Science Edition), 2009, 30(4): 68- 71.

Google Scholar