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《Engineering of Surveying and Mapping》 2006-04
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Application of hopfield neural network in leveling network adjustment

NIE Jian-liang~1,LIAO Shun-shan~2(1.School of Geological and Surveying Engineering,Changan University,Xian 710054,China;2.Road and Bridge Southern China Engineering CO.LTD,Zhongshan 528403,China)  
Hopfield neural network(HNN) has strong optimal capacity in model parameter estimation.A new kind of energy function is constructed for HNN by introducing IGGⅢ equivalent weight function.A conjugate gradient algorithm is applied to improving the efficiency of HNN.The robustness of Sigmoid function is discussed.It proves that Hopfield neural network is efficient in refining model when data errors follow normal distribution.When data are contaminated by outliers,the robustness of parameter estimation can be applied by using the IGGⅢ equivalent weight function.A simulated example for a static leveling circle is given,which shows that the improved HNN with IGGⅢ equivalent weight function is robust and efficient.
【Fund】: 国家自然科学基金项目(40274002和40474001)
【CateGory Index】: P207.2
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