DocumentCode
3290211
Title
Notice of Retraction
The technology of adaptive detection of life signals based on RBF neural network
Author
Li jian-jun
Author_Institution
Inf. Eng. Coll., Inner Mongolia Sci. & Technol. Univ., Baotou, China
fYear
2011
fDate
15-17 April 2011
Firstpage
4685
Lastpage
4686
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.
Aimed at the adaptive offset technology, a adaptive detection algorithm based on RBF neural network is proposed to improve LMS algorithm of slow convergence speed and extraction of narrow band signal faults. This algorithm does not need a priori knowledge of the input signal , the characterize of the algorithm is strong ability of the nonlinear mapping and self-learning and small amount of calculation and real-time, the effect is better at using this system in the field of life signal detection. Simulation and experiment analysis results indicate the validity and the practicability of the method proposed.
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.
Aimed at the adaptive offset technology, a adaptive detection algorithm based on RBF neural network is proposed to improve LMS algorithm of slow convergence speed and extraction of narrow band signal faults. This algorithm does not need a priori knowledge of the input signal , the characterize of the algorithm is strong ability of the nonlinear mapping and self-learning and small amount of calculation and real-time, the effect is better at using this system in the field of life signal detection. Simulation and experiment analysis results indicate the validity and the practicability of the method proposed.
Keywords
convergence of numerical methods; least mean squares methods; radial basis function networks; signal detection; unsupervised learning; LMS algorithm; RBF neural network; adaptive detection algorithm; adaptive offset technology; convergence speed; life signal detection; narrow band signal fault extraction; nonlinear mapping; self-learning; Adaptation model; Adaptive systems; Analytical models; Artificial neural networks; Data mining; Heart beat; Knowledge engineering; Neural Network; Noise Cancellation; adaptive filter;
fLanguage
English
Publisher
ieee
Conference_Titel
Electric Information and Control Engineering (ICEICE), 2011 International Conference on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-8036-4
Type
conf
DOI
10.1109/ICEICE.2011.5778148
Filename
5778148
Link To Document