The Study on the Application of GA-BP Neural Network in the Pneumatic Pressure-Type Relative Altimeter

In order to improve the accuracy of the relative height measurement,a high-precision pneumatic relative altimeter has been studied and implemented through pressure sensor-array measurements and data fusion with an optimizing back propagation(BP)neural network algorithm based on genetic algorithm(GA). The corresponding hardware and software designs have been provided as well. Combined with experimental measured data and relevant literature data,the application performances of the GA-BP neural network,the traditional BP neural network and standard formulas in the pneumatic relative altimeter were compared and analyzed in term of accuracy,stability and versatility. The results show that the proposed pneumatic relative altimeter based on GA-BP neural network has higher accuracy,higher stability and better ability to promote,and it can meet the daily needs of real-time measurement for the relative height.