Research on modified wavelet online sequential extreme learning machine in space registration for photoelectric theodolite
Yang Hong-ta · China Measurement & Test · 2015
An algorithm using composite functions and wavelet neural networks( WNN) in online sequential extreme learning machines( OS-ELM) was proposed to solve the problem in the space registration of photoelectric theodolite data fusion system. The wavelet theory was introduced to extreme learning machines and the wavelet function and bounded non-constant piecewise continuous function were used to build an hidden-node excitation function for extreme learning machine. The contraction-expansion and shift factors of the wavelet function were initiated with the input data range and it was trained in combination with the online learning methods of extreme learning machine. Experimental results show that this algorithm can improve the measurement accuracy of photoelectric theodolite to within 3″ and has fast online learning speed and good generalization compared with standard space registration algorithms.