DocumentCode
1932117
Title
Notice of Retraction
A rule-optimization algorithm based on fuzzy neural network
Author
Yushu Yang ; Cao Ran ; Guo Wei
Author_Institution
Eng. Coll., Northeast Agric. Univ., Haerbin, China
Volume
1
fYear
2010
fDate
9-11 July 2010
Firstpage
267
Lastpage
270
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
A new network for fuzzy-neural system was proposed based on the analysis and comparison of existing methods, which could be easy to distill the fuzzy rules. The network structure was adjusted by FBP(Fuzzy Back Propagation) learning algorithm to acquire network parameters and variable weights. By aiming at disadvantage of IP algorithm on rule-optimization, the Improved Iterative Pruning Neural Network (IIP) algorithm could lessen the network structure and reduce the complexity of compute to speed up the respond rate of output. The simulation results by fertilizer knowledge model demonstrate the effectiveness and feasibility of proposed rule-optimization algorithm based on FNN.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
A new network for fuzzy-neural system was proposed based on the analysis and comparison of existing methods, which could be easy to distill the fuzzy rules. The network structure was adjusted by FBP(Fuzzy Back Propagation) learning algorithm to acquire network parameters and variable weights. By aiming at disadvantage of IP algorithm on rule-optimization, the Improved Iterative Pruning Neural Network (IIP) algorithm could lessen the network structure and reduce the complexity of compute to speed up the respond rate of output. The simulation results by fertilizer knowledge model demonstrate the effectiveness and feasibility of proposed rule-optimization algorithm based on FNN.
Keywords
backpropagation; fuzzy neural nets; optimisation; IP algorithm; fertilizer knowledge model; fuzzy backpropagation learning algorithm; fuzzy neural network system; fuzzy rules; iterative pruning neural network algorithm; network structure; rule-optimization algorithm; Algorithm design and analysis; Artificial neural networks; Optimization; FMLP Network; fuzzy rule-inference; iterative pruning algorithm; soil fertilizer knowledge model;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-5537-9
Type
conf
DOI
10.1109/ICCSIT.2010.5563752
Filename
5563752
Link To Document