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Sensitivity Study and Application of Complex Variabal Weight Function Neural Network

Author: DingMan
Tutor: ZhangDaiYuan
School: Nanjing University of Posts and Telecommunications
Course: Applied Computer Technology
Keywords: Neural Networks Complex variable weight function Complex variable approximation Sensitivity Spectrum shaping
CLC: TP183
Type: Master's thesis
Year: 2012
Downloads: 11
Quote: 0
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Abstract


Monograph \This new concept can overcome the defects of traditional neural network training , while avoiding local minima , slow convergence many shortcomings . Complex variable weighting function neural network is a complex field expansion , so it has all the benefits of the weight function neural network . The sensitivity of the neural network , but also has important research significance . Calculate the weight function of the neural network in the training , the network output by the surrounding environment and input disturbances can be drawn through the sensitivity analysis . In this paper, the neural network sensitivity analysis as the premise , in-depth study of the network model error and approximation noise error , and thus to study the sensitivity of the complex variable weighting function neural network , and derived formula . The article first introduces the representation of complex function of variable weights ; then introduce the weight function neural network sensitivity statistical definition ; then study network model error noise and approximation error and eventually come to the sensitivity of the complex variable weighting function neural network . Meanwhile, on the basis of theoretical analysis using Matlab tools and simulation of complex variable weighting function neural network and its sensitivity , through the analysis of simulation results verify the validity and correctness of the theoretical analysis . The sensitivity of the use of complex variable weighting function neural network , this paper design a cognitive radio spectrum shaping . Based on the theoretical analysis , to design a model of the spectrum shaping circumvention band ; thus calculate the system 's sensitivity to achieve reliable spectrum sharing . Finally, after the experimental simulation validation, combined with the complex variable weighting function neural network sensitivity spectrum shaping in the effective use of spectrum and avoid spectrum collisions have better results.

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