With the development of modern pressure transmitter detection technology and artificial intelligence technology,stricter requirements are made on the stability,real-time performance and measurement accuracy of pressure transmitter in aerospace,petrochemical,nuclear power and other fields.The temperature of working environment may greatly affect the accuracy of equipment,leading to offset in the measured values of the transmitter.Aiming at these problems,this paper proposes a temperature compensation method for high-precision pressure transmitters based on adaptive DBN(Deep Belief Network).DBN extracts features from data in the unsupervised learning phase,and then fine tunes network parameters using a small amount of data in the supervised phase;Utilizing the BWO(Beluga whale optimization)algorithm to achieve a balance between global search and local optimization,effectively improving the optimization performance of DBN networks;Introducing the Metropolis criterion and fitness balance factor to further improve the algorithm's global search capability and model convergence speed.The experimental results show that the data accuracy after fitting can reach 0.004 8％,much higher than the existing highest standard of 0.05 level.By comparative analysis,the accuracy and practicability of the compensation algorithm are verified.
关键词
温度补偿/深度置信网络/启发式算法/压力变送器/白鲸优化算法
Key words
temperature compensation/deep belief network/heuristic algorithm/pressure transmitter/Beluga whale optimization
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